Prediction of Heart Disease using Machine Learning Algorithm
Viraj S. Varale
Abstract
Human hearts suffer through various heart ailments. There are several diseases related to the heart like cardiomyopathy, Aorta diseases, Coronary Heart Disease (CHD) and arrhythmia which majorly contributes mortality and morbidity rates worldwide. One in 4 deaths in India is now because of cardiovascular disease with ischemic heart disease. The biggest challenge to overcome is the prediction of cardiovascular diseases via data analysis in the clinical domain. Now a day's large number of data is produced in health care and wellness industry. Finding meaningful data and patterns is the urgent need to make the proper regulations and forecasting. We proposed a framework for predicting a heart disease using three different algorithms: Random forest, Naive Bayes, and logistic regression. Proposed system uses Cleveland dataset from machine learning UCI repository for training and testing of the model. This model imbibes various significant features and classification techniques to predict the results. We also compare the results of proposed system with the algorithms that are existing in the literature, on the same dataset and it is observed that model produce an enhanced accuracy performance of 94.73%.