An Efficient Method for Heart Disease Prediction Using Hybrid Classifier Model in Machine Learning

Abstract

Heart disease is the foremost and leading cause of major death rate all over the world today. Cardiovascular disease prediction is considered to be an extremely challenging factor in the clinical decision support system (CDSS). Recently, Machine learning (ML) has shown its significance effectively for making predictions and assistance for a huge amount of data generated by the healthcare industries. Similarly, ML approaches have been adopted in various research developments in the E-healthcare monitoring field. Many existing investigations have given a glimpse for disease prediction using ML approaches. Here, a novel method is known as the Hybrid Linear stacking model for feature selection and Xgboost algorithm for heart disease classification (HLS-Xgboost). This model initially predicts the significant or most influencing features of the disease and is classified using the Xgboost algorithm for enhancing prediction accuracy. This model is initiated with various feature subset combinations and diverse classification approaches. This model produces an improved performance level with 96%accuracy using the HLS-Xgboost model. The proposed model gives a better trade-off with any prediction accuracy of 96% when compared to other approaches.