A new machine learning technique for an accurate diagnosis of coronary artery disease. (October 2019)

• Record Type: Journal Article
• Authors: Abdar, Moloud; Książek, Wojciech; Acharya, U Rajendra; Tan, Ru-San; Makarenkov, Vladimir; Pławiak, Paweł
• Is Part Of: Computer methods and programs in biomedicine. Volume 179(2019)
• Publication Date: 2019-10
• Subjects: Coronary artery disease (CAD) -- Machine learning -- Normalization -- Genetic algorithm -- Particle swarm optimization -- Feature selection -- Classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.104992
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Novel data mining method is proposed for CAD diagnosis. Application of feature selection (based on GA and PSO) is proposed. New genetic training (N2Genetic optimizer) based on fusion of 10-fold cross-validation with GA or PSO is employed. SVM (SVC, nuSVM, LinSVM) is employed for classification. High classification accuracy of 93.08% is obtained. 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.