A Machine Learning Model for Predicting Heart Disease using Ensemble Methods

Abstract

There is the continuous increase in death rate related to cardiac disease across the world. Prediction of the heart disease in advance may help the experts to suggest the pre-emptive measures to minimize the death risk. The early diagnosis of heart disease symptoms is made possible by machine learning technologies. The existing machine learning models are inefficient in terms of simulation error, accuracy and timing for heart disease prediction. Hence, an efficient approach is needed for efficient prediction of heart disease. In the current research paper, a model based on Machine learning techniques has been proposed for early and accurate prediction of heart disease. The proposed model is based on techniques for feature optimization, feature selection, and ensemble learning. Using WEKA 3.8.3 tool, the feature selection and feature optimisation technique has been applied for irrelevant features elimination and then the pragmatic features are tested using ensemble techniques. Further, the comparison of the proposed model is made with the existing model without feature selection and feature optimisation technique in terms of heart disease prediction effectiveness. It is found that the results of proposed model gives the better performance in terms of simulation error, response time and accuracy in heart disease prediction.