Improving Accuracy of Heart Disease Prediction through Machine Learning Algorithms

One of the main causes of death in the world is heart disease. Early heart disease detection and treatment can lower mortality rates and enhance quality of life which is the major challenge. “Machine learning” algorithms can accurately predict the likelihood of getting the heart disease by using data like: “age, gender, lifestyle factors, medical history, and laboratory testing”. Building a ML model for “heart disease prediction” which is merely relies on the various relevant factors is the primary goal of this paper. For this research project, we used 4 different datasets which comprises of distinct factors that are relevant to heart disease. The model building is made through ML algorithms: “Random forest, K-nearest neighbour, logistic regression, and decision tree”. The study demonstrates that, when compared to other ML techniques, logistic regression and KNN provide better prediction accuracy in a shorter amount of time.