Moloud Abdar1, Wojciech Książek2, U Rajendra Acharya3, Ru-San Tan4, Vladimir Makarenkov1, Paweł Pławiak5. 1. Département d'informatique, Université du Québec à Montréal, Montréal, Québec, Canada. 2. Institute of Telecomputing, Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, 31-155 Krakow, Poland; Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland. 3. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia. 4. Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore. 5. Institute of Telecomputing, Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, 31-155 Krakow, Poland. Electronic address: plawiak@pk.edu.pl.
Abstract
BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients. METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features. RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field. CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
BACKGROUND AND OBJECTIVE:Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients. METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features. RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field. CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
Authors: Valeria Cantoni; Roberta Green; Carlo Ricciardi; Roberta Assante; Leandro Donisi; Emilia Zampella; Giuseppe Cesarelli; Carmela Nappi; Vincenzo Sannino; Valeria Gaudieri; Teresa Mannarino; Andrea Genova; Giovanni De Simini; Alessia Giordano; Adriana D'Antonio; Wanda Acampa; Mario Petretta; Alberto Cuocolo Journal: Comput Math Methods Med Date: 2021-10-16 Impact factor: 2.238
Authors: L J Muhammad; Ibrahem Al-Shourbaji; Ahmed Abba Haruna; I A Mohammed; Abdulkadir Ahmad; Muhammed Besiru Jibrin Journal: SN Comput Sci Date: 2021-06-22