Machine Learning Algorithms for The Classification of Cardiovascular Disease- A Comparative Study
Wada Mohammed Jinjri, Pantea Keikhosrokiani, Nasuha Lee Abdullah
Abstract
Heart disease (cardiovascular disease) is a major human disorder that significantly affects many people's lives. Diagnosing heart disease becomes an important task to reduce its sovereignty in its early stage. Machine learning methods remain the most widely used for the classification and detection processes. This work aims to design and identify a model that best classifies cardiovascular disease and predicts the presence or absence of the disease in patients using machine learning methods with accurate predictions. Therefore, this paper compares the five most powerful machine learning platforms to classify cardiovascular disease data. The proposed five different classifiers are are support vector machine (SVM), K-nearest neighbor (K-NN), Logistic regression (LR), Decision tree (DT), and Naive Bayes (NB) for the classification of cardiovascular disease (CVD). To validate the work, the dataset was obtained from the Kaggle repository online. The algorithms' performance is analyzed, evaluated, and compared by applying various performance factors. Results indicates that support vector machine (SVM) and logistic regression (LR) methods are the most efficient for diagnosing cardiovascular disease.