An Effective Machine Learning-Based Model for an Early Heart Disease Prediction

Abstract

Heart disease (HD) has become a dangerous problem and one of the most significant mortality factors worldwide, which requires an expensive and sophisticated detection process. Most people are affected due to the failure of the heart which seriously threatens their lives due to high morbidity and mortality. Therefore, accurate prediction and diagnosis are needed for early prevention, detection, and treatment to reduce the death threats to human life. However, an early and accurate prediction of HD is still a challenging task to be addressed. In this work, we propose a machine learning-based prediction model (MLbPM) that exploits a combination of the data scaling methods, the split ratios, the best parameters, and the machine learning algorithms for predicting HD. The performance of the proposed model is tested by performing experiments on a University of California Irvine HD dataset to indicate the presence or absence of HD. The results show that the proposed MLbPM provides an accuracy of 96.7% when logistic regression, robust scaler, best parameter, and 70 : 30 as a split ratio of the dataset are considered. In addition, MLbPM outperforms other compared works in terms of accuracy.