Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier

Abstract

Heart updated failure is a very serious medical issue nowadays. It causes a lot of deaths all over the world. The bad lifestyle, bad eating habits, unusual food timings are some of the factors responsible for this disease. Artificial intelligence and machine learning is a technology which is used by many researchers for prediction of diseases. Machine Learning (ML) algorithms provide some models which are first trained on a training data and then can be used to test the input data. These models are very helpful in prediction of heart disease. In this work XGBoost, Logistic Regression and Support Vector Machine ML models are used to predict heart disease. Cross validation method is used in this work which improved the prediction accuracy of all the three models. Outcoming results ensure that the XGBoost classifier is the best ML model for heart disease prediction as compared to Logistic Regression and Support vector Machine.