Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction

In the last few years, cardiovascular diseases have emerged as one of the most common causes of deaths worldwide. The lifestyle changes, eating habits, working cultures etc, has significantly contributed to this alarming issue across the globe including the developed, underdeveloped and developing nations. Early detection of the initial signs of cardiovascular diseases and the continuous medical supervision can help in reducing rising number of patients and eventually the mortality rate. However with limited medical facilities and specialist doctors, it is difficult to continuously monitor the patients and provide consultations. Technological interventions are required to facilitate the patient monitoring and treatment. The healthcare data generated through various medical procedures and continuous patient monitoring can be utilized to develop efficient prediction models for cardiovascular diseases. The early prognosis of cardiovascular illnesses can aid in making decisions on life-style changes in high hazard sufferers and in turn lessen the complications, which may be an outstanding milestone inside the field of medicine. This paper studies some of the most widely used machine learning algorithms for heart disease prediction by using the medical data and historical information. The various techniques are discussed and a comparative analysis of the same is presented. This report compares five common strategies for predicting the chance of heart attack that have been published in the literature. KNN, Decision Tree, Gaussian Naive Bayes, Logistic Regression, and Random Forest are some of the approaches used. Further, the paper also highlights the advantages and disadvantages of using the various techniques for developing the prediction models