Cardiovascular Disease is the leading cause of death (Approximately, 17 million people every year) in the all the area of the world. Prediction of heart disease is the critical challenge in the area of the clinical data analysis. The objective of paper is to build the model for predicting the Heart Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of heart disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model. The result of the study indicates that KNN algorithm is effective in predicting the model with the accuracy of the 85.71% and having a very low mean squared error.
An Improved Machine Learning Technique with Effective Heart Disease Prediction System