PCA-Based Feature Extraction for Classification of Heart Disease

Abstract

AbstractResearchers have extensively employed machine learning (ML) approaches to establish forecasting systems in numerous domains like software faults, spam identification, disease foresting, and fraud recognition. The detection of individuals susceptible to heart disease (HD) could assist medical practitioners in their verdict concerning treatments and management. This study, therefore, is projected to examine the discriminative power of numerous prognosticators in the research to intensify the efficiency of HD discovery through their warning signs. A dimensionality reduction method called principal component analysis (PCA) and three machine learning (ML) classifiers which are Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) are employed in the study. The three ML classifiers were implemented with PCA DR. The techniques’ performance was evaluated on a dataset attained from National Health Service (NHS) database. The system was evaluated using confusion matrix measures for instance, accuracy, precision, specificity, f1-score, detection rate, and false-positive rate (FPR). A comparative analysis was conducted between the three ML classifiers with PCA DR. It was concluded that the classifiers.KeywordsMachine learningPCASupport vector machineRandom forestNaïve Bayes