Performance Analysis of Machine Learning Algorithms to Predict Cardiovascular Disease

Abstract

Globally the rate of heart disease has increased drastically due to unhealthy eating habits and reduced physical activities. It has become one of the significant causes of death worldwide. As per the reports of the world health organization(WHO), 31% of all deaths worldwide are caused by cardiovascular diseases. This demands the development of a system capable of early detection of cardiovascular diseases at an affordable cost. With this as the objective, multiple machine learning algorithms have been selected to evaluate their performance in the early detection of cardiovascular diseases. This work utilizes available data sets of an individual’s vital parameters, demographic data, and exercise parameters for predicting cardiovascular diseases. An extensive evaluation is performed to identify the best-suited supervised machine learning classifier that could predict cardiovascular diseases using the available datasets. This research work details the nine different classification algorithms utilized for this analysis. For each algorithm, the F1-score, precision, recall, accuracy, and Area Under the Receiver Operating Characteristics (AUROC) values for each model have been determined and compared with the rest of the algorithms. The results show that random forest and gradient boosting models outperform others and demonstrate an F1-Score of 0.88 and an AUROC value of 0.92, respectively. This showcases that doctors could utilize this technique for the early identification of cardiovascular diseases. This will provide the opportunity to offer adequate medical treatments early, thus saving lives.