A Hybrid Method for Heart Disease Diagnosis Using Integrated Feature Selection and Optimized Classification Approaches

Abstract :

Heart disease is one of the leading causes of mortality worldwide, and its early diagnosis is of great importance. Existing feature selection methods for heart disease diagnosis are typically limited to using a single algorithm, which may lead to the selection of redundant features or the omission of important ones, consequently reducing classification accuracy. In this paper, a novel hybrid method for feature selection is proposed, which identifies more efficient and relevant features by employing a soft integration of the results from multiple feature selection algorithms. To enhance the accuracy and speed of diagnosis, an Extreme Learning Machine (ELM) classifier with a wavelet kernel is utilized, where its parameters are optimized using a modified version of the Shuffled Frog-Leaping Algorithm (SFLA). The improved algorithm incorporates a dynamic weighting mechanism and is combined with a Genetic Algorithm (GA), contributing to improved classification accuracy and speed. To demonstrate the robustness and generalizability of the proposed method, it is tested on three well-known UCI datasets. Evaluation results show that the proposed model achieves an accuracy of 93.3%. These findings highlight the high capability and generalization power of the proposed method in heart disease diagnosis.

References:

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