1
|
Eyupoglu C, Karakuş O. Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques. J Clin Med 2024; 13:2868. [PMID: 38792410 PMCID: PMC11122190 DOI: 10.3390/jcm13102868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features-age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality-and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.
Collapse
Affiliation(s)
- Can Eyupoglu
- Department of Computer Engineering, Turkish Air Force Academy, National Defence University, Istanbul 34149, Türkiye;
| | - Oktay Karakuş
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK
| |
Collapse
|
2
|
Miriyala GP, Sinha AK. PSO-XnB: a proposed model for predicting hospital stay of CAD patients. Front Artif Intell 2024; 7:1381430. [PMID: 38765633 PMCID: PMC11100420 DOI: 10.3389/frai.2024.1381430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/11/2024] [Indexed: 05/22/2024] Open
Abstract
Coronary artery disease poses a significant challenge in decision-making when predicting the length of stay for a hospitalized patient. This study presents a predictive model-a Particle Swarm Optimized-Enhanced NeuroBoost-that combines the deep autoencoder with an eXtreme gradient boosting model optimized using particle swarm optimization. The model uses a fuzzy set of rules to categorize the length of stay into four distinct classes, followed by data preparation and preprocessing. In this study, the dimensionality of the data is reduced using deep neural autoencoders. The reconstructed data obtained from autoencoders is given as input to an eXtreme gradient boosting model. Finally, the model is tuned with particle swarm optimization to obtain optimal hyperparameters. With the proposed technique, the model achieved superior performance with an overall accuracy of 98.8% compared to traditional ensemble models and past research works. The model also scored highest in other metrics such as precision, recall, and particularly F1 scores for all categories of hospital stay. These scores validate the suitability of our proposed model in medical healthcare applications.
Collapse
Affiliation(s)
| | - Arun Kumar Sinha
- School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| |
Collapse
|
3
|
Zhou K, Yin Z, Gu J, Zeng Z. A Feature Selection Method Based on Graph Theory for Cancer Classification. Comb Chem High Throughput Screen 2024; 27:650-660. [PMID: 37056061 DOI: 10.2174/1386207326666230413085646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/02/2023] [Accepted: 02/24/2023] [Indexed: 04/15/2023]
Abstract
OBJECTIVE Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification. METHODS In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data. RESULTS In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods. CONCLUSION In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.
Collapse
Affiliation(s)
- Kai Zhou
- School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Zhixiang Yin
- School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Jiaying Gu
- School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Zhiliang Zeng
- School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China
| |
Collapse
|
4
|
Zhan EB, Du HW. Safety and effectiveness of nano composite hydrogel stent implantation in the treatment of coronary cardiovascular disease: A preclinical study. Prev Med 2023; 172:107524. [PMID: 37127121 DOI: 10.1016/j.ypmed.2023.107524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/03/2023]
Abstract
With the improvement of people's quality of life, various cardiovascular diseases are the most common diseases. Therefore, the main site of disease atherosclerosis is blood vessels, so we can see that its flow rate has obvious changes. Through the analysis of coronary heart disease, this paper studies the relationship between coronary artery disease and cardiovascular disease, which is helpful to evaluate the risk of disease, and also provides the best prevention and treatment plan to overcome cardiovascular disease. As the material of artificial cartilage repair, nanocomposite hydrogel has excellent application value and attraction, because nanocomposite hydrogel has a structure similar to the extracellular matrix of natural chondrocytes. The patients in the experimental group were treated with nano composite hydrogel stent implantation. The other group of patients used the traditional way to carry out the comparative experiment. In the perfusion data of each ventricular wall in the coronary angiography and anterior wall perfusion group, the percentage of lateral wall in the normal proportion was the highest, 69.2%, 59.3% in the anterior wall, 39.5% in the inferior wall, and 19.7% in the apical value and interval. The percentage of LAD stenosis in anterior wall perfusion was O. The highest percentage in the lateral wall was 69.2%, and the lowest in the septum and apex was 19.7%. Nanocomposite hydrogel stent implantation can effectively treat coronary heart disease. The research shows that it is safe and effective in application.
Collapse
Affiliation(s)
- En-Bo Zhan
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
| | - Hong-Wei Du
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China.
| |
Collapse
|
5
|
Kampaktsis PN, Emfietzoglou M, Al Shehhi A, Fasoula NA, Bakogiannis C, Mouselimis D, Tsarouchas A, Vassilikos VP, Kallmayer M, Eckstein HH, Hadjileontiadis L, Karlas A. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med 2023; 9:949454. [PMID: 36741834 PMCID: PMC9896100 DOI: 10.3389/fcvm.2022.949454] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 12/28/2022] [Indexed: 01/21/2023] Open
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.
Collapse
Affiliation(s)
- Polydoros N. Kampaktsis
- Division of Cardiology, Columbia University Irving Medical Center, New York, NY, United States,*Correspondence: Polydoros N. Kampaktsis,
| | - Maria Emfietzoglou
- Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Aamna Al Shehhi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Nikolina-Alexia Fasoula
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany
| | - Constantinos Bakogiannis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Mouselimis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasios Tsarouchas
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilios P. Vassilikos
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Healthcare Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Angelos Karlas
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany,Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| |
Collapse
|
6
|
Navin K, Nehemiah HK, Nancy Jane Y, Veena Saroji H. A classification framework using filter–wrapper based feature selection approach for the diagnosis of congenital heart failure. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-221348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor.
Collapse
Affiliation(s)
- K.S. Navin
- Ramanujan Computing Centre, Anna University, Chennai, India
| | | | - Y. Nancy Jane
- Department of Computer Technology, Madras Institute of Technology, Chennai, India
| | - H. Veena Saroji
- Assistant Director Planning, Directorate of Health Services, Kerala, India
| |
Collapse
|
7
|
Khalili H, Rismani M, Nematollahi MA, Masoudi MS, Asadollahi A, Taheri R, Pourmontaseri H, Valibeygi A, Roshanzamir M, Alizadehsani R, Niakan A, Andishgar A, Islam SMS, Acharya UR. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep 2023; 13:960. [PMID: 36653412 PMCID: PMC9849475 DOI: 10.1038/s41598-023-28188-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
Collapse
Affiliation(s)
- Hosseinali Khalili
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maziyar Rismani
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | | | - Mohammad Sadegh Masoudi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Reza Taheri
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Hossein Pourmontaseri
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran.,Bitab Knowledge Enterprise, Fasa University of Medical Sciences, Fasa, Iran
| | - Adib Valibeygi
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Amin Niakan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aref Andishgar
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia.,Cardiovascular Division, The George Institute for Global Health, Newtown, Australia.,Sydney Medical School, University of Sydney, Camperdown, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
| |
Collapse
|
8
|
Mohammedqasim H, Mohammedqasem R, Ata O, Alyasin EI. Diagnosing Coronary Artery Disease on the Basis of Hard Ensemble Voting Optimization. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121745. [PMID: 36556946 PMCID: PMC9783937 DOI: 10.3390/medicina58121745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
Background and Objectives: Recently, many studies have focused on the early diagnosis of coronary artery disease (CAD), which is one of the leading causes of cardiac-associated death worldwide. The effectiveness of the most important features influencing disease diagnosis determines the performance of machine learning systems that can allow for timely and accurate treatment. We performed a Hybrid ML framework based on hard ensemble voting optimization (HEVO) to classify patients with CAD using the Z-Alizadeh Sani dataset. All categorical features were converted to numerical forms, the synthetic minority oversampling technique (SMOTE) was employed to overcome imbalanced distribution between two classes in the dataset, and then, recursive feature elimination (RFE) with random forest (RF) was used to obtain the best subset of features. Materials and Methods: After solving the biased distribution in the CAD data set using the SMOTE method and finding the high correlation features that affected the classification of CAD patients. The performance of the proposed model was evaluated using grid search optimization, and the best hyperparameters were identified for developing four applications, namely, RF, AdaBoost, gradient-boosting, and extra trees based on an HEV classifier. Results: Five fold cross-validation experiments with the HEV classifier showed excellent prediction performance results with the 10 best balanced features obtained using SMOTE and feature selection. All evaluation metrics results reached > 98% with the HEV classifier, and the gradient-boosting model was the second best classification model with accuracy = 97% and F1-score = 98%. Conclusions: When compared to modern methods, the proposed method perform well in diagnosing coronary artery disease, and therefore, the proposed method can be used by medical personnel for supplementary therapy for timely, accurate, and efficient identification of CAD cases in suspected patients.
Collapse
|
9
|
Sayadi M, Varadarajan V, Sadoughi F, Chopannejad S, Langarizadeh M. A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111933. [PMID: 36431068 PMCID: PMC9698583 DOI: 10.3390/life12111933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022]
Abstract
Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis. The proposed model can diagnose CAD based on clinical data and without the use of an invasive procedure. Methods: In this paper, machine-learning (ML) techniques were used for the early detection of CAD, which were applied to a CAD dataset known as Z-Alizadeh Sani. Since this dataset has 54 features, the Pearson correlation feature selection method was conducted to identify the most effective features. Then, six machine learning techniques including decision tree, deep learning, logistic regression, random forest, support vector machine (SVM), and Xgboost were employed based on a semi-random-partitioning framework. Result: Applying Pearson feature selection to the dataset demonstrated that only eight features were the most effective for CAD diagnosis. The results of running the six machine-learning models on the selected features showed that logistic regression and SVM had the same performance with 95.45% accuracy, 95.91% sensitivity, 91.66% specificity, and a 96.90% F1 score. In addition, the ROC curve indicates a similar result regarding the AUC (0.98). Conclusions: Prediction is an important component of medical decision support systems. The results of the present study showed that feature selection has a high impact on machine-learning performance and, regardless of the evaluation metrics of the machine-learning models, determining the effective features is very important. However, SVM and Logistic Regression were designated as the best models according to our selected features.
Collapse
Affiliation(s)
- Mohammadjavad Sayadi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Department of Computer Engineering, Technical and Vocational University (TVU), Tehran 14357-61137, Iran
| | - Vijayakumar Varadarajan
- School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia
- Dean International, Ajeenkya D Y Patil University, Pune 412105, India
- Swiss School of Business and Management, 1213 Geneva, Switzerland
- Correspondence: (V.V.); (M.L.)
| | - Farahnaz Sadoughi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Sara Chopannejad
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Mostafa Langarizadeh
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Correspondence: (V.V.); (M.L.)
| |
Collapse
|
10
|
Wadhawan S, Maini R. ETCD: An effective machine learning based technique for cardiac disease prediction with optimal feature subset selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
11
|
Azadifar S, Rostami M, Berahmand K, Moradi P, Oussalah M. Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput Biol Med 2022; 147:105766. [DOI: 10.1016/j.compbiomed.2022.105766] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/12/2022] [Accepted: 06/18/2022] [Indexed: 11/26/2022]
|
12
|
Książek W, Turza F, Pławiak P. NCA-GA-SVM: A new two-level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3599. [PMID: 35403827 DOI: 10.1002/cnm.3599] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.
Collapse
Affiliation(s)
- Wojciech Książek
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Kraków, Poland
| | - Filip Turza
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Kraków, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland
| |
Collapse
|
13
|
Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm. ELECTRONICS 2022. [DOI: 10.3390/electronics11091495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.
Collapse
|
14
|
A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2585235. [PMID: 35299686 PMCID: PMC8923755 DOI: 10.1155/2022/2585235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/30/2021] [Accepted: 08/13/2021] [Indexed: 11/19/2022]
Abstract
Machine intelligence can convert raw clinical data into an informational source that helps make decisions and predictions. As a result, cardiovascular diseases are more likely to be addressed as early as possible before affecting the lifespan. Artificial intelligence has taken research on disease diagnosis and identification to another level. Despite several methods and models coming into existence, there is a possibility of improving the classification or forecast accuracy. By selecting the connected combination of models and features, we can improve accuracy. To achieve a better solution, we have proposed a reliable ensemble model in this paper. The proposed model produced results of 96.75% on the cardiovascular disease dataset obtained from the Mendeley Data Center, 93.39% on the comprehensive dataset collected from IEEE DataPort, and 88.24% on data collected from the Cleveland dataset. With this proposed model, we can achieve the safety and health security of an individual.
Collapse
|
15
|
Almustafa KM. Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2022; 34:e6675. [PMID: 34899078 PMCID: PMC8646298 DOI: 10.1002/cpe.6675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/15/2021] [Accepted: 09/24/2021] [Indexed: 06/04/2023]
Abstract
Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.
Collapse
Affiliation(s)
- Khaled Mohamad Almustafa
- Department of Information Systems, College of Computer and Information SystemsPrince Sultan UniversityRiyadhKingdom of Saudi Arabia
| |
Collapse
|
16
|
Hassannataj Joloudari J, Azizi F, Nematollahi MA, Alizadehsani R, Hassannatajjeloudari E, Nodehi I, Mosavi A. GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis. Front Cardiovasc Med 2022; 8:760178. [PMID: 35187099 PMCID: PMC8855497 DOI: 10.3389/fcvm.2021.760178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Methods Hence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset. Results As a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset. Conclusion We demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
Collapse
Affiliation(s)
| | - Faezeh Azizi
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | | | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, Australia
| | - Edris Hassannatajjeloudari
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
| | - Issa Nodehi
- Department of Computer Engineering, University of Qom, Qom, Iran
| | - Amir Mosavi
- Faculty of Informatics, Technische Universität Dresden, Dresden, Germany
- Faculty of Civil Engineering, TU-Dresden, Dresden, Germany
- John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary
- Institute of Information Society, University of Public Service, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| |
Collapse
|
17
|
Shao Z, Yuan S, Xu J, Wang Y. A statistical feature data mining framework for constructing scholars’ career trajectories in academic data. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
18
|
Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology. ELECTRONICS 2022. [DOI: 10.3390/electronics11030315] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Coronary artery disease (CAD) is one of the diseases with the highest morbidity and mortality in the world. In 2019, the number of deaths caused by CAD reached 9.14 million. The detection and treatment of CAD in the early stage is crucial to save lives and improve prognosis. Therefore, the purpose of this research is to develop a machine-learning system that can be used to help diagnose CAD accurately in the early stage. In this paper, two classical ensemble learning algorithms, namely, XGBoost algorithm and Random Forest algorithm, were used as the classification model. In order to improve the classification accuracy and performance of the model, we applied four feature processing techniques to process features respectively. In addition, synthetic minority oversampling technology (SMOTE) and adaptive synthetic (ADASYN) were used to balance the dataset, which included 71.29% CAD samples and 28.71% normal samples. The four feature processing technologies improved the performance of the classification models in terms of classification accuracy, precision, recall, F1 score and specificity. In particular, the XGBboost algorithm achieved the best prediction performance results on the dataset processed by feature construction and the SMOTE method. The best classification accuracy, recall, specificity, precision, F1 score and AUC were 94.7%, 96.1%, 93.2%, 93.4%, 94.6% and 98.0%, respectively. The experimental results prove that the proposed method can accurately and reliably identify CAD patients from suspicious patients in the early stage and can be used by medical staff for auxiliary diagnosis.
Collapse
|
19
|
Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-Rokny H. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2381-2402. [PMID: 35240789 DOI: 10.3934/mbe.2022110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
Collapse
Affiliation(s)
- Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam 786004, IN
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, IR
| | | | - Afshin Shoeibi
- FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Mehdi Sookhak
- Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU
| |
Collapse
|
20
|
Wadhawan S, Maini R. A Systematic Review on Prediction Techniques for Cardiac Disease. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2022. [DOI: 10.4018/ijitsa.290001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mortality rate can be lowered with early prediction of cardiac diseases, which is one of the major issue in healthcare industry. In comparison of traditional methods, intelligent systems have potential to predict these diseases accurately at early stage even with complex data. Various intelligent DSS are presented by researchers for predicting this disease. To study the trends of these intelligent systems, to find the effective techniques for predicting cardiac disease and to find the future directions are the objective of this study. Therefore this paper presents a systematic review on state-of-art techniques based on ML, NN and FL. For analysis, we follow PRISMA statement and considered the studies presented from 2010 to 2020 from different databases. Analysis concluded that ML based techniques are broadly used for feature selection and classification and have the potential for the prediction of cardiac diseases. The future directions are to evaluate the rarely used prediction techniques and finding the way of improving them for model generalization with better prediction accuracy.
Collapse
Affiliation(s)
- Savita Wadhawan
- Department of CSE, Punjabi University, Patiala, India & MMICTBM, MM(DU), Mullana, Ambala, India
| | - Raman Maini
- Department of CSE, Punjabi University, Patiala, India
| |
Collapse
|
21
|
Nahavandi D, Alizadehsani R, Khosravi A, Acharya UR. Application of artificial intelligence in wearable devices: Opportunities and challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106541. [PMID: 34837860 DOI: 10.1016/j.cmpb.2021.106541] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/07/2021] [Accepted: 11/15/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data. METHODS Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives. RESULTS Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays. CONCLUSION The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.
Collapse
Affiliation(s)
- Darius Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
| |
Collapse
|
22
|
Beheshti Roui M, Zomorodi M, Sarvelayati M, Abdar M, Noori H, Pławiak P, Tadeusiewicz R, Zhou X, Khosravi A, Nahavandi S, Acharya UR. A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
|
23
|
Syrkiewicz-Świtała M, Detyna B, Sosada N, Detyna J, Świtała R, Bitkowska A, Szkutnik J. Mobile applications and eating habits among women and men – Polish experiences. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
24
|
Feature selection using hybrid poor and rich optimization algorithm for text classification. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.03.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
25
|
C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02467-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
|
26
|
An Improved Hybrid Approach for Handling Class Imbalance Problem. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 46:3853-3864. [PMID: 33532169 PMCID: PMC7841761 DOI: 10.1007/s13369-021-05347-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 01/12/2021] [Indexed: 11/28/2022]
Abstract
Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.
Collapse
|
27
|
Fahami MA, Roshanzamir M, Izadi NH, Keyvani V, Alizadehsani R. Detection of effective genes in colon cancer: A machine learning approach. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
|
28
|
Velusamy D, Ramasamy K. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105770. [PMID: 33027698 DOI: 10.1016/j.cmpb.2020.105770] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/19/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Coronary artery disease (CAD) is considered one of the most prominent health issues causing high mortality in the world population. Hence, earlier diagnosis and prediction of CAD is essential for the proper medication of patients. The objective of this study is to develop a machine learning algorithm that will help in accurate diagnosis of CAD. METHODS In this paper, we have proposed a novel heterogeneous ensemble method combining three base classifiers viz., K-Nearest Neighbour, Random Forest, and Support Vector Machine for effective diagnosis of CAD. The results of base classifiers are combined using ensemble voting technique based on average-voting (AVEn), majority-voting (MVEn), and weighted-average voting (WAVEn) for prediction of CAD. The random forest-based Boruta wrapper feature selection algorithm and feature importance of SVM are used for relevant feature selection based on attribute importance and rank. RESULTS The proposed ensemble algorithm is developed using 5 features selected based on the feature importance and the performance of the algorithm is evaluated using the Z-Alizadeh Sani dataset. Further, the dataset is balanced using Synthetic Minority Over-sampling Technique and its performance is evaluated. The result analysis shows that the WAVEn algorithm achieves better classification accuracy, sensitivity, specificity and precision of 98.97%, 100%, 96.3% and 98.3% respectively for the original dataset. The WAVEn algorithm applied on the balanced dataset achieves 100% accuracy, sensitivity, specificity and precision in diagnosing CAD. To the best of author's knowledge, the accuracy achieved by WAVEn is the highest accuracy when compared with the state-of-the-art algorithms in the literature for both original and balanced dataset. CONCLUSIONS The statistical results prove the robustness of the WAVEn algorithm in reliably discriminating the CAD patients from healthy ones with high precision, and therefore it can be used for developing a decision support system for diagnosing CAD at an early stage.
Collapse
Affiliation(s)
- Durgadevi Velusamy
- Department of Computer Science and Engineering, M.Kumarasamy College of Engineering, Karur, Tamilnadu, 639 113, India.
| | - Karthikeyan Ramasamy
- Department of Electrical and Electronics Engineering, M.Kumarasamy College of Engineering, Karur, Tamilnadu, 639 113, India.
| |
Collapse
|
29
|
Martis RJ, Lin H, Javadi B, Fernandes SL, Yasmin M. Editorial of the special issue DLHI: Deep learning in medical imaging and healthinformatics. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.09.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
30
|
Chen X, Dai J, Lin J, Wu Y, Ouyang J, Huang M, Zhuang J, Fang Y, Wu J. Image-based morphometric studies of human coronary artery bifurcations with/without coronary artery disease. Comput Methods Biomech Biomed Engin 2020; 24:1-17. [PMID: 33252247 DOI: 10.1080/10255842.2020.1850702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/03/2020] [Accepted: 11/09/2020] [Indexed: 10/22/2022]
Abstract
It is of great clinical significance to study the relationship between coronary bifurcation's morphometrical feature change and coronary artery disease (CAD) lesion. The purpose of this study is to determine the morphological changes in patients with CAD lesion when compared with non-CAD subjects and to find indicators that may be used for cardiovascular disease diagnosis. Computed tomography angiography images from Southern Chinese populations were used to reconstruct three-dimensional coronary arterial trees. Murray's law was introduced to assess the level of deviation of the realistic vascular networks from their optimal state. The results showed CAD Left had the highest deviation values of ARR (0.2552 ± 0.0071 ) and DERR (0.5059 ± 0.0098 ), while non-CAD Right had the lowest values (ARR : 0.1892 ± 0.0066 and DERR : 0.3733 ± 0.0092 , respectively). Moreover, the slope values of the ratio between D m 3 and D s 3 + D l 3 for non-CAD Left, CAD Left, non-CAD Right, and CAD Right were 0.7428, 0.7004, 0.7628, and 0.7577, respectively. Theoretically, the slope value should equal to 1 when the bifurcation structure is in its optimal state. Therefore, these results indicated that coronary bifurcations with CAD lesion deviated from the optimal structure further than those without CAD lesion and coronary bifurcations in right were closer to the optimal structure than those in left. More importantly, the present study found that DERR and AER depended only on the diseased state, but not age, suggesting that DERR and AER were potentially used as two novel indicators for early CAD diagnosis.
Collapse
Affiliation(s)
- Xueping Chen
- Institute of Biomechanics, School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, P.R. China
| | - Jingxing Dai
- Guangdong Provincial Key Laboratory of Medicine and Biomechanics, Department of Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, P.R. China
| | - Jiangguo Lin
- Institute of Biomechanics, School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, P.R. China
- Research Department of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Yueheng Wu
- Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Jun Ouyang
- Guangdong Provincial Key Laboratory of Medicine and Biomechanics, Department of Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, P.R. China
| | - Meiping Huang
- Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangzhou, P.R. China
| | - Jian Zhuang
- Research Department of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Ying Fang
- Institute of Biomechanics, School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, P.R. China
| | - Jianhua Wu
- Institute of Biomechanics, School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, P.R. China
| |
Collapse
|
31
|
Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217656] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background: Early accurate detection of coronary artery disease (CAD) is one of the most important medical research areas. Researchers are motivated to utilize machine learning techniques for quick and accurate detection of CAD. Methods: To obtain the high quality of features used for machine learning, we here extracted the coronary bifurcation features from the coronary computed tomography angiography (CCTA) images by using the morphometric method. The machine learning classifier algorithms, such as logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), artificial neural network (ANN), and support vector machine (SVM) were applied for estimating the performance by using the measured features. Results: The results showed that in comparison with other machine learning methods, the polynomial-SVM with the use of the grid search optimization method had the best performance for the detection of CAD and had yielded the classification accuracy of 100.00%. Among six examined coronary bifurcation features, the exponent of vessel diameter (n) and the area expansion ratio (AER) were two key features in the detection of CAD. Conclusions: This study could aid the clinicians to detect CAD accurately, which may probably provide an alternative method for the non-invasive diagnosis in clinical.
Collapse
|
32
|
Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D, Khozeimeh F, Shoeibi A, Nahavandi S, Panahiazar M, Bishara A, Beygui RE, Puri R, Kapadia S, Tan RS, Acharya UR. Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020. Comput Biol Med 2020; 128:104095. [PMID: 33217660 DOI: 10.1016/j.compbiomed.2020.104095] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/24/2020] [Accepted: 10/24/2020] [Indexed: 02/06/2023]
Abstract
While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption.
Collapse
Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Mohamad Roshanzamir
- Department of Engineering, Fasa Branch, Islamic Azad University, Post Box No 364, Fasa, Fars, 7461789818, Iran
| | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Khorram Ave, Isfahan, Iran; Faculty of Medicine, SPPH, University of British Columbia, Vancouver, BC, Canada.
| | - Davood Shafie
- Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran; Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California, San Francisco, USA
| | - Andrew Bishara
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, USA
| | - Ramin E Beygui
- Cardiovascular Surgery Division, Department of Surgery, University of California, San Francisco, CA, USA
| | - Rishi Puri
- Department of Cardiovascular Medicine, Cleveland Clinic, OH, USA
| | - Samir Kapadia
- Department of Cardiovascular Medicine, Cleveland Clinic, OH, USA
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
| |
Collapse
|
33
|
Książek W, Hammad M, Pławiak P, Acharya UR, Tadeusiewicz R. Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
34
|
Almustafa KM. Prediction of heart disease and classifiers' sensitivity analysis. BMC Bioinformatics 2020; 21:278. [PMID: 32615980 PMCID: PMC7331233 DOI: 10.1186/s12859-020-03626-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/22/2020] [Indexed: 12/20/2022] Open
Abstract
Background Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases. Results It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases. Conclusion Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.
Collapse
Affiliation(s)
- Khaled Mohamad Almustafa
- Department of Information Systems, College of Computer and Information Systems, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.
| |
Collapse
|
35
|
Baskaran L, Ying X, Xu Z, Al’Aref SJ, Lee BC, Lee SE, Danad I, Park HB, Bathina R, Baggiano A, Beltrama V, Cerci R, Choi EY, Choi JH, Choi SY, Cole J, Doh JH, Ha SJ, Her AY, Kepka C, Kim JY, Kim JW, Kim SW, Kim W, Lu Y, Kumar A, Heo R, Lee JH, Sung JM, Valeti U, Andreini D, Pontone G, Han D, Villines TC, Lin F, Chang HJ, Min JK, Shaw LJ. Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study. PLoS One 2020; 15:e0233791. [PMID: 32584909 PMCID: PMC7316297 DOI: 10.1371/journal.pone.0233791] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 05/12/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization. METHODS For prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost). RESULTS Mean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P <0.0001). CONCLUSIONS For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance.
Collapse
Affiliation(s)
- Lohendran Baskaran
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
- Department of Cardiovascular Medicine, National Heart Centre, Singapore, Singapore
| | - Xiaohan Ying
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Zhuoran Xu
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Subhi J. Al’Aref
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Benjamin C. Lee
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Sang-Eun Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Integrative Cardiovascular Imaging Center, Yonsei University College of Medicine, Seoul, South Korea
| | | | - Hyung-Bok Park
- Myongji Hospital, Seonam University College of Medicine, Gyeonggi-do, South Korea
| | - Ravi Bathina
- CARE Hospital and FACTS Foundation, Hyderabad, India
| | | | | | | | | | | | - So-Yeon Choi
- Ajou University Hospital, Gyeonggi-do, South Korea
| | - Jason Cole
- Cardiology Associates of Mobile, Mobile, Alabama, United States of America
| | - Joon-Hyung Doh
- Inje University, Ilsan Paik Hospital, Gyeonggi-do, South Korea
| | - Sang-Jin Ha
- Gangneung Asan Hospital, Gangwon-do, South Korea
| | - Ae-Young Her
- Kangwon National University Hospital, Gangwon-do, South Korea
| | | | | | - Jin-Won Kim
- Korea University Guro Hospital, Seoul, South Korea
| | | | - Woong Kim
- Yeungnam University Hospital, Daegu, South Korea
| | - Yao Lu
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Amit Kumar
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Ran Heo
- Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Ji Hyun Lee
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
- Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea
| | - Ji-min Sung
- Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea
| | - Uma Valeti
- Department of Medicine, Stanford Medicine, Stanford, California, United States of America
| | | | | | - Donghee Han
- Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, Los Angeles, California, United States of America
| | - Todd C. Villines
- Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, United States of America
| | - Fay Lin
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital, Integrative Cardiovascular Imaging Center, Yonsei University College of Medicine, Seoul, South Korea
| | - James K. Min
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
- Cleerly, Inc, New York, New York, United States of America
| | - Leslee J. Shaw
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York, United States of America
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
| |
Collapse
|
36
|
Terrada O, Cherradi B, Raihani A, Bouattane O. A novel medical diagnosis support system for predicting patients with atherosclerosis diseases. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100483] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
|