Heart Failure Prediction Using Machine Learning Algorithms with Cross Validations

Heart failure is common problem among the people. Many people suffer from this problem. Bad eating habits, BMI level, obesity, in active daily life are the things which are responsible for heart failure. Many data sets about patients are available which stores information about heart of the human being. Machine Learning (ML) algorithms are applied for making prediction of various diseases in recent years. ML models are trained on data sets and then trained models are tested on the tested data sets. Cross validation is a new tool to improve ML. In this paper accuracy of five ML models is calculated with and without cross validation. From results it is observed that cross validation improves the accuracy of all five ML models. The Random Forest ML model is best for heart disease prediction with 92.86% accuracy.