oronary artery disease (CAD) is a major global health issue. Early detection plays a crucial role in reducing risk and improving patient outcomes. This study proposes a novel, efficient approach to CAD diagnosis by integrating a histogram-based feature selection method with a specially designed long short-term memory (LSTM) classifier. The method is evaluated on two benchmark datasets: Z-Alizadeh Sani and Cleveland. Imbalanced class distribution, a common challenge in medical datasets, is addressed using the synthetic minority over-sampling technique (SMOTE). The proposed feature selection technique offers a fast and simple alternative to traditional optimization methods like particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), and the whale optimization algorithm (WOA), which typically require extensive parameter tuning and longer processing times. The histogram-based method selects features based on their distribution similarity to a Gaussian profile, aiming to enhance classification performance and computational efficiency. The selected features are then classified using a custom-designed LSTM architecture optimized through Grid Search and validated via k-fold cross-validation (k-fold). The effectiveness of the proposed method is demonstrated by comparing it with other feature selection approaches using metrics such as accuracy, precision, sensitivity, and the F1-score (f-score). Experimental results show that the histogram-based method significantly improves classification accuracy and reduces computational time. This approach offers a promising, low-cost, and scalable solution for CAD diagnosis, especially in resource-constrained settings, and provides valuable contributions to the field of medical data analysis.

Coronary artery disease, histogram-based feature selection, SMOTE method, TLBO method, LSTM architecture