Published Paper Details:

Heart disease is a commonly occurring disease and is the major cause of sudden death now a days. This disease attacks the person instantly. Most people are not aware of the symptoms of heart disease. Timely attention and proper diagnosis of heart disease will reduce the mortality rate. Medical data mining is to explore hidden patterns from the data sets. In this project, we have applied big data tools namely spark, and also using ML algorithms like Logistic Regression, Decision Tree, Random Forest, and Navie Bayes predict the heart disease. Result shows that KNN gives high accuracy score with 85% compare to other algorithms LR is 76%, RF is 78%, DT is 73%and NB is 79%.In order to improve the accuracy, we have done Feature Selection. Feature selection measure discards redundant features to improve the accuracy of the classifier. Our proposed method effectively identifies the redundant features compared to other existing features to effectively predict heart disease. This project helps to predict the future possibility of heart disease in patients