Shokouhifar, M.; Hasanvand, M.; Moharamkhani, E.; Werner, F. Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data. Algorithms2024, 17, 34.
Shokouhifar, M.; Hasanvand, M.; Moharamkhani, E.; Werner, F. Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data. Algorithms 2024, 17, 34.
Shokouhifar, M.; Hasanvand, M.; Moharamkhani, E.; Werner, F. Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data. Algorithms2024, 17, 34.
Shokouhifar, M.; Hasanvand, M.; Moharamkhani, E.; Werner, F. Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data. Algorithms 2024, 17, 34.
Abstract
Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in pre-venting adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an Ensemble Heuristic-Metaheuristic Feature Fusion Learning (EHMFFL) algorithm for heart disease diagnosis. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic-metaheuristic approach that integrates the heuristic knowledge of Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning, aimed at constructing a robust prediction model. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art machine learning, ensemble learning, and feature selection techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
