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Punacha G, Adiga R. Feature selection for effective prediction of SARS-COV-2 using machine learning. Genes Genomics 2024; 46:341-354. [PMID: 37985549 DOI: 10.1007/s13258-023-01467-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 10/01/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND With rise in variants of SARS-CoV-2, it is necessary to classify the emerging SARS-CoV-2 for early detection and thereby reduce human transmission. Genomic and proteomic information have less frequently been used for classifying in a machine learning (ML) approach for detection of SARS-CoV-2. OBJECTIVE With this aim we used nucleoprotein and viral proteomic evolutionary information of SARS-CoV-2 along with the charge and basicity distribution of amino acids from various strains of SARS-CoV-2 to generate a disease severity model based on ML. METHODS All sequence and clinical data were obtained from GISAID. Proteomic level calculations were added to comprise the dataset. The training set was used for feature selection. Select K- Best feature selection method was employed which was cross validated with testing set and performance evaluated. Delong's test was also done. We also employed BIRCH clustering on SARS-CoV-2 for clustering the strains. RESULTS Out of six ML models four were successful in training and testing. Extra Trees algorithm generated a micro-averaged F1-score of 74.2% and a weighted averaged area under the receiver operating characteristic curve (AUC-ROC) score of 73.7% with multi-class option. The feature selection set to 5, enhanced the ROC AUC from 73.7 to 76.4%. Accuracy of the selected model of 86.9% was achieved. CONCLUSION The unique features identified in the ML approach was able to classify disease severity into classes and had potential for predicting risk in newer variants.
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Affiliation(s)
- Gagan Punacha
- Nitte (Deemed to be University), Department of Molecular Genetics & Cancer, Nitte University Centre for Science Education & Research (NUCSER), Mangalore, Karnataka, India
| | - Rama Adiga
- Nitte (Deemed to be University), Department of Molecular Genetics & Cancer, Nitte University Centre for Science Education & Research (NUCSER), Mangalore, Karnataka, India.
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Wen C, Liu S, Liu S, Heidari AA, Hijji M, Zarco C, Muhammad K. ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans. Comput Biol Med 2023; 153:106338. [PMID: 36640529 PMCID: PMC9678829 DOI: 10.1016/j.compbiomed.2022.106338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 11/08/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022]
Abstract
Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.
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Affiliation(s)
- Cuihong Wen
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, 100871, China
| | - Shaowu Liu
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Shuai Liu
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; School of Educational Science, Hunan Normal University, Changsha, 410081, China; Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha, 410081, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran.
| | - Mohammad Hijji
- Faculty of Computers and Information Technology (FCIT), University of Tabuk, Tabuk, 47711, Saudi Arabia
| | - Carmen Zarco
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, South Korea.
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Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G. Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2683-2723. [PMID: 36685136 PMCID: PMC9838547 DOI: 10.1007/s11831-023-09883-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.
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Affiliation(s)
| | - Alaettin Ucan
- Department of Computer Engineering, Osmaniye Korkut Ata University, Osmaniye, Turkey
| | - Turgay Ibrikci
- Department of Software Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Bahman Arasteh
- Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey
| | - Gultekin Isik
- Department of Computer Engineering, Igdir University, Igdir, Turkey
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Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9043300. [PMID: 35785145 PMCID: PMC9246600 DOI: 10.1155/2022/9043300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 04/28/2022] [Accepted: 05/13/2022] [Indexed: 11/18/2022]
Abstract
Background Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment. Methods GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457. Results In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait. Conclusions CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.
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Karlafti E, Anagnostis A, Kotzakioulafi E, Vittoraki MC, Eufraimidou A, Kasarjyan K, Eufraimidou K, Dimitriadou G, Kakanis C, Anthopoulos M, Kaiafa G, Savopoulos C, Didangelos T. Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction. J Pers Med 2021; 11:1380. [PMID: 34945852 PMCID: PMC8705973 DOI: 10.3390/jpm11121380] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/07/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open
Abstract
Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial intelligence (AI) and machine learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classifications which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear. Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa). More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying a principal component analysis (PCA) on the numerical part of the dataset and a multiple correspondence analysis (MCA) on the categorical part of the dataset. Then, the Bayesian information criterion (BIC) was applied to Gaussian mixture models (GMM) in order to identify the optimal number of clusters under which the best grouping of patients occurs. The proposed methodology identified four clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%. This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and the patient's mortality.
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Affiliation(s)
- Eleni Karlafti
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
- Emergency Department, AHEPA University Hospital, Aristotle University of Thessaloniki, 54621 Thessaloniki, Greece
| | - Athanasios Anagnostis
- Advanced Insights, Artificial Intelligence Solutions, Ipsilantou 10, Panorama, 55236 Thessaloniki, Greece;
| | - Evangelia Kotzakioulafi
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Michaela Chrysanthi Vittoraki
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Ariadni Eufraimidou
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Kristine Kasarjyan
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Katerina Eufraimidou
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Georgia Dimitriadou
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Chrisovalantis Kakanis
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Michail Anthopoulos
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Georgia Kaiafa
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Christos Savopoulos
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Triantafyllos Didangelos
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
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Hu J, Han Z, Heidari AA, Shou Y, Ye H, Wang L, Huang X, Chen H, Chen Y, Wu P. Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine. Comput Biol Med 2021; 142:105166. [PMID: 35077935 PMCID: PMC8701842 DOI: 10.1016/j.compbiomed.2021.105166] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 01/08/2023]
Abstract
Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.
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Luo XQ, Yan P, Zhang NY, Luo B, Wang M, Deng YH, Wu T, Wu X, Liu Q, Wang HS, Wang L, Kang YX, Duan SB. Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis. Sci Rep 2021; 11:20269. [PMID: 34642418 PMCID: PMC8511088 DOI: 10.1038/s41598-021-99840-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/30/2021] [Indexed: 12/29/2022] Open
Abstract
Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74-0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73-0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.
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Affiliation(s)
- Xiao-Qin Luo
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Ping Yan
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Ning-Ya Zhang
- Information Center, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Bei Luo
- Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, 999077, Hong Kong SAR, China
| | - Mei Wang
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Ying-Hao Deng
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Ting Wu
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Xi Wu
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Qian Liu
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Hong-Shen Wang
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Lin Wang
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Yi-Xin Kang
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China
| | - Shao-Bin Duan
- Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
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Shakeel CS, Khan SJ, Chaudhry B, Aijaz SF, Hassan U. Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:1102083. [PMID: 34434248 PMCID: PMC8382550 DOI: 10.1155/2021/1102083] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/18/2021] [Accepted: 07/29/2021] [Indexed: 01/29/2023]
Abstract
Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k-nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T-test showed significant differences between the two accuracies with a p < 0.001. SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.
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Affiliation(s)
- Choudhary Sobhan Shakeel
- Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan
| | - Saad Jawaid Khan
- Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan
| | - Beenish Chaudhry
- School of Computing and Informatics, University of Louisiana at Lafayette, USA
| | - Syeda Fatima Aijaz
- Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan
| | - Umer Hassan
- Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan
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Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation. Comput Biol Med 2021; 136:104609. [PMID: 34293587 PMCID: PMC8254401 DOI: 10.1016/j.compbiomed.2021.104609] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 01/22/2023]
Abstract
This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.
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Cobre ADF, Stremel DP, Noleto GR, Fachi MM, Surek M, Wiens A, Tonin FS, Pontarolo R. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput Biol Med 2021; 134:104531. [PMID: 34091385 PMCID: PMC8164361 DOI: 10.1016/j.compbiomed.2021.104531] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE This study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity. METHODS COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve). RESULTS The first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase. CONCLUSION Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.
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Affiliation(s)
| | - Dile Pontarolo Stremel
- Department of Forest Engineering and Technology, Universidade Federal Do Paraná, Curitiba, Brazil
| | | | - Mariana Millan Fachi
- Pharmaceutical Sciences Postgraduate Programme, Universidade Federal Do Paraná, Curitiba, Brazil
| | - Monica Surek
- Pharmaceutical Sciences Postgraduate Programme, Universidade Federal Do Paraná, Curitiba, Brazil
| | - Astrid Wiens
- Department of Pharmacy, Universidade Federal Do Paraná, Curitiba, Brazil
| | - Fernanda Stumpf Tonin
- Pharmaceutical Sciences Postgraduate Programme, Universidade Federal Do Paraná, Curitiba, Brazil
| | - Roberto Pontarolo
- Department of Pharmacy, Universidade Federal Do Paraná, Curitiba, Brazil,Corresponding author
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