A Comparison of Neural Networks and Machine Learning Methods for Prediction of Heart Disease

Abstract

Heart disease is a major cause of death and disability across the world. Heart disease mortality and morbidity rates can be greatly decreased with early detection and treatment. Hence, the development of efficient and accurate methods for early diagnosis of heart disease has become a priority in the medical field. In this study, we did a comparative study of exiting supervised machine learning approaches for predicting heart disease diagnosis and also improved the accuracy of KNN by changing K values. We used a dataset that consists of a variety of features such as age, gender and other important indicators for heart disease diagnosis. We then explored and evaluated traditional ML algorithms such as logistic regression, decision tree, random forest and SVM for the predictive analysis. A number of criteria, including accuracy, precision, recall, and F1 Score, were used to assess the models' performance. This study provides evidence that ML algorithms can be used to forecast the diagnosis of heart disease. Healthcare providers and medical practitioners can utilize the outcomes of this study for early detection and management of cardiac disease. Further research will aim to analyse and evaluate additional machine learning algorithms to enhance precision and performance.