Abstract

AbstractNowadays, heart diseases are significantly contributing to deaths all over the world. Thus, heart-disease prediction has garnered considerable attention in the medical domain globally. Accordingly, machine-learning algorithms for the early prediction of heart diseases were developed in several studies to help physicians design medical procedures. In this study, a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) optimized approach based on random forest (RF), called GAPSO-RF, is developed and used to select the optimal features that can increase the accuracy of heart-disease prediction. The proposed GAPSO-RF implements multivariate statistical analysis in the first step to select the most significant features used in the initial population. After that, a discriminate mutation strategy is implemented in GA. GAPSO-RF combines a modified GA for global search and a PSO for local search. Moreover, PSO achieved the concept of rehabbing individuals that had been refused in the selection process. The performance of the proposed GAPSO-RF approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the receiver operating characteristic (ROC) curve by using two datasets from the University of California, namely, Cleveland and Statlog. The experimental results confirm that the GAPSO-RF approach attained the high heart-disease-prediction accuracies of 95.6% and 91.4% on the Cleveland and Statlog datasets, respectively. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.

Highlights

  • The heart pumps blood to the entire human body

  • We introduce an efficient, hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach based on random forest (RF) for optimizing the Feature selection (FS) process to select the crucial features that increase the accuracy of heart-disease diagnosis

  • We presented a GAPSO-RF-based FS approach with an RF classifier as the base of a fitness function to select significant features to increase the accuracy of heart-disease diagnosis

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Summary

Introduction

The heart pumps blood to the entire human body. Coronary arteries are the blood-vessels that transport oxygenated blood to the heart [36]. In 2013, heart diseases caused the highest number of deaths globally, at approximately 17.3 million. In 2016, approximately 17.6 million deaths were attributed to heart diseases, amounting to a rise of 14.5% from 2006 [10]. Heart diseases may be managed or controlled if trained medical professionals detect them at their early stages, thereby enabling them to make the correct decision. Early detection of heart diseases is critical to improving HF symptoms and extending the lives of patients [8]. The medical history of a patient includes a substantial number of features. Not all these features may be significant, and some may even be redundant. Most research-based on heart-disease prediction focused on two factors: selecting the best features while dismissing the irrelevant ones and choosing an appropriate classifier. Machine-learning-based methods have improved the quality of our lives, especially in the medical domain [2, 5, 7, 20, 46, 48, 49, 53]

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