Heart Disorders Forecasting with Feature Engineering using Non-Parametric Classifier

Heart disorders are one of the significant causes of demise around the globe, almost 17.9 million individuals have lost their lives due to heart disorder. Early prediction of the disorders is necessary to combat it effectively but timely prediction of heart disorders is a very complex and challenging task for healthcare professionals requiring complex and expensive techniques. Machine learning is one of the most efficient techniques that can be used for early prediction of heart disorders. Machine learning algorithms are used in order to find patterns and other insights in the data sets in order to form an efficient model to predict heart disorders. In this research we have used four different algorithms which are logistic regression, KNN, random forest and XGBoost out of these algorithms random forest has provided the best results with an accuracy of 88.04% along with the recall score of 85.0