Abstract
Heart disease, encompassing a range of conditions affecting the heart, remains a leading cause of morbidity and mortality worldwide. The urgent need for precise diagnostic techniques is crucial for improving patient outcomes, as early and accurate diagnosis can significantly influence the effectiveness of treatment and management strategies. This study introduces an innovative approach to diagnosing heart disease by combining classifiers in a meta‐learning‐based approach and utilizing advanced feature selection methods in a hybrid model. Using the extensive heart disease dataset provided by the IEEE DataPort, our method aims to enhance the accuracy of diagnosis by gradually refining the selection of relevant features at two separate stages. Initially, in level 0, a collection of filter‐based algorithms, such as mutual information, relief, ANOVA, systematic uncertainty, and minimum redundancy‐maximum relevance, is utilized to identify a subset of relevant features. Subsequently, the Sequential Forward Selection (SFS) wrapper method is employed in level 1 to further fine‐tune the feature set. The model utilizes a meta‐learning‐based ensemble strategy for classification, combining basic classifiers such as K‐nearest neighbor (KNN), Naïve Bayes (NB), decision tree (DT), and extreme gradient boost (XGBoost) in level 0. In addition, a logistic regression (LR) meta‐classifier is incorporated at level 1. The proposed hybrid technique has been tested on the heart disease dataset, and the results show that the meta‐learning‐based hybrid feature selection approach performs exceptionally well in terms of performance metrics. The model produces impressive results with only eight precisely chosen features, including an accuracy of 96.2185%, precision of 96.0317%, recall of 96.8%, F1‐score of 96.4143%, and an area under the curve (AUC) of 0.9619. Furthermore, our approach significantly outperforms state‐of‐the‐art techniques, indicating its potential to revolutionize heart disease diagnosis and improve patient care.