1
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Nematollahi MA, Jahangiri S, Asadollahi A, Salimi M, Dehghan A, Mashayekh M, Roshanzamir M, Gholamabbas G, Alizadehsani R, Bazrafshan M, Bazrafshan H, Bazrafshan Drissi H, Shariful Islam SM. Body composition predicts hypertension using machine learning methods: a cohort study. Sci Rep 2023; 13:6885. [PMID: 37105977 PMCID: PMC10140285 DOI: 10.1038/s41598-023-34127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.
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Affiliation(s)
| | - Soodeh Jahangiri
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Non Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Maryam Salimi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
- Bone and Joint Diseases Research Center, Department of Orthopedic Surgery, Shiraz University of Medical Science, Shiraz, Iran
| | - Azizallah Dehghan
- Non Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Mina Mashayekh
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Ghazal Gholamabbas
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | | | - Hanieh Bazrafshan
- Department of Neurology, Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamed Bazrafshan Drissi
- Cardiovascular Research Center, Shiraz University of Medical Sciences, PO Box: 71348-14336, Shiraz, 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
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2
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Alizadehsani R, Roshanzamir M, Izadi NH, Gravina R, Kabir HMD, Nahavandi D, Alinejad-Rokny H, Khosravi A, Acharya UR, Nahavandi S, Fortino G. Swarm Intelligence in Internet of Medical Things: A Review. Sensors (Basel) 2023; 23:s23031466. [PMID: 36772503 PMCID: PMC9920579 DOI: 10.3390/s23031466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 05/13/2023]
Abstract
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
- Correspondence:
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Vali asr Blvd, Fasa 74617-81189, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Daneshgah e Sanati Hwy, Isfahan 84156-83111, Iran
| | - Raffaele Gravina
- Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy
| | - H. M. Dipu Kabir
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - Darius Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW 2109, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
- Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy
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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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4
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Alizadehsani R, Eskandarian R, Behjati M, Zahmatkesh M, Roshanzamir M, Izadi NH, Shoeibi A, Haddadi A, Khozeimeh F, Sani FA, Sani ZA, Roshanzamir Z, Khosravi A, Nahavandi S, Sarrafzadegan N, Islam SMS. Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID‐19. Immun Inflamm Dis 2022; 10:e561. [PMID: 35048534 PMCID: PMC8926510 DOI: 10.1002/iid3.561] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 12/01/2022] Open
Abstract
Introduction To reduce mortality in hospitalized patients with COVID‐19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID‐19. Methods This study was conducted on 660 hospitalized patients with CVD and COVID‐19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID‐19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation. Results Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID‐19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation. Conclusions Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for COVID‐19.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Australia
| | - Rahimeh Eskandarian
- Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran
| | - Mohaddeseh Behjati
- Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran
| | - Mehrdad Zahmatkesh
- Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran
| | | | - Navid H. Izadi
- Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran
| | - 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
| | - Azadeh Haddadi
- Department of Biology, Faculy of Basic Sciences, Shahrekord Branch Islamic Azad University Shahrekord Iran
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Australia
| | - Fariba A. Sani
- Faculty of Medicine Mashhad University of Medical Science Mashhad Iran
| | | | - Zahra Roshanzamir
- Pediatric Respiratory and Sleep Medicine Research Center, Children's Medical Center Tehran University of Medical Sciences Tehran Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Australia
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute Isfahan University of Medical Sciences Isfahan Iran
- Faculty of Medicine, SPPH University of British Columbia Vancouver British Columbia Canada
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences Deakin University Geelong Victoria Australia
- Cardiovascular Division The George Institute for Global Health Newtown Australia
- Sydney Medical School University of Sydney Camperdown Australia
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5
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Alizadehsani R, Roshanzamir M, Hussain S, Khosravi A, Koohestani A, Zangooei MH, Abdar M, Beykikhoshk A, Shoeibi A, Zare A, Panahiazar M, Nahavandi S, Srinivasan D, Atiya AF, Acharya UR. Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020). Ann Oper Res 2021:1-42. [PMID: 33776178 PMCID: PMC7982279 DOI: 10.1007/s10479-021-04006-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/23/2021] [Indexed: 05/17/2023]
Abstract
Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189 Fasa, Iran
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Dibrugarh, Assam 786004 India
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Afsaneh Koohestani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | | | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Adham Beykikhoshk
- Applied Artificial Intelligence Institute, 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
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California, San Francisco, USA
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Dipti Srinivasan
- Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo, 12613 Egypt
| | - 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, Taiwan
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6
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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] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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7
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Alizadehsani R, Alizadeh Sani Z, Behjati M, Roshanzamir Z, Hussain S, Abedini N, Hasanzadeh F, Khosravi A, Shoeibi A, Roshanzamir M, Moradnejad P, Nahavandi S, Khozeimeh F, Zare A, Panahiazar M, Acharya UR, Islam SMS. Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients. J Med Virol 2020; 93:2307-2320. [PMID: 33247599 PMCID: PMC7753243 DOI: 10.1002/jmv.26699] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/06/2020] [Accepted: 11/20/2020] [Indexed: 12/24/2022]
Abstract
Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Zahra Alizadeh Sani
- Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.,Department of Cardiac MRI, Omid Hospital, Tehran, Iran
| | - Mohaddeseh Behjati
- Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Roshanzamir
- Pediatric Respiratory and Sleep Medicine Research Center, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sadiq Hussain
- System Administrator at Dibrugarh University, Dibrugarh, Assam, India
| | - Niloofar Abedini
- Tehran University of Medical Science, Imam Khomeini Hospital Complex, Tehran, Iran
| | | | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Afshin Shoeibi
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran.,Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Pardis Moradnejad
- Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
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8
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Alizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Khosravi A, Panahiazar M, Koohestani A, Khozeimeh F, Nahavandi S, Sarrafzadegan N. A database for using machine learning and data mining techniques for coronary artery disease diagnosis. Sci Data 2019; 6:227. [PMID: 31645559 PMCID: PMC6811630 DOI: 10.1038/s41597-019-0206-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 08/16/2019] [Indexed: 12/28/2022] Open
Abstract
We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports.
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Affiliation(s)
- R Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - M Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - M Abdar
- Département d'informatique, Université du Québec à Montréal, Montréal, Québec, Canada
| | - A Beykikhoshk
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - A Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - M Panahiazar
- University of California San Francisco, San Francisco, CA, USA.
| | - A Koohestani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - F Khozeimeh
- Mashhad University of Medical Science, Mashhad, Iran
| | - S Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - N Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, Nahavandi S, Sarrafzadegan N, Acharya UR. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Comput Biol Med 2019; 111:103346. [PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/26/2019] [Accepted: 06/26/2019] [Indexed: 02/02/2023]
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia.
| | - Moloud Abdar
- Département d'informatique, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Parham M Kebria
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Nizal Sarrafzadegan
- Faculty of Medicine, SPPH, University of British Columbia, Vancouver, BC, Canada; Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Khorram Ave, Isfahan, Iran
| | - 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 Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
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Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N, Nahavandi S. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. Comput Methods Programs Biomed 2018; 162:119-127. [PMID: 29903478 DOI: 10.1016/j.cmpb.2018.05.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 04/24/2018] [Accepted: 05/03/2018] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
| | - Mohammad Javad Hosseini
- Department of Computer Science and Engineering, University of Washington, Seattle, United States
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia.
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences,Isfahan,Iran & Faculty of Medicine, SPPH, University of British Columbia, Vancouver,BC, Canada
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
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Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods Programs Biomed 2017; 141:19-26. [PMID: 28241964 DOI: 10.1016/j.cmpb.2017.01.004] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 12/18/2016] [Accepted: 01/12/2017] [Indexed: 05/28/2023]
Abstract
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.
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Affiliation(s)
- Zeinab Arabasadi
- Department of Computer Engineering, University of Bojnord, Bojnord, Iran
| | - Roohallah Alizadehsani
- Department of Computer Engineering, Sharif University of Technology, Azadi Ave, Tehran, Iran.
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Iran
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Khozeimeh F, Alizadehsani R, Roshanzamir M, Khosravi A, Layegh P, Nahavandi S. An expert system for selecting wart treatment method. Comput Biol Med 2017; 81:167-175. [DOI: 10.1016/j.compbiomed.2017.01.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 12/31/2016] [Accepted: 01/03/2017] [Indexed: 01/15/2023]
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Alizadehsani R, Zangooei MH, Hosseini MJ, Habibi J, Khosravi A, Roshanzamir M, Khozeimeh F, Sarrafzadegan N, Nahavandi S. Coronary artery disease detection using computational intelligence methods. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.07.004] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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