OPTIMIZING CONVOLUTIONAL NEURAL NETWORKS WITH SIMULATED ANNEALING FOR HEART DISEASE PREDICTION

Authors : Osama Burak Elhalid, Mehmet Fatih Demiral

Pages : 1023-1033

Doi:10.21923/jesd.1638469

View : 211 | Download : 230

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, underscoring the urgent need for reliable predictive models that can support early diagnosis and effective treatment. This study introduces a novel framework that combines Convolutional Neural Networks (CNNs) with the Simulated Annealing (SA) algorithm to optimize critical hyperparameters, including the number of filters, kernel size, hidden units, and batch size. The experiments were conducted on the publicly available Cleveland Heart Disease dataset from the UCI Machine Learning Repository, which contains 303 patient records with 14 clinical attributes. The proposed SA-CNN model achieved an accuracy of 96.1% and an F1-score of 0.96, surpassing baseline CNNs and traditional optimization techniques such as grid search and random search. By systematically navigating the hyperparameter space, the SA algorithm reduced overfitting and improved the model’s generalization ability. These findings highlight the effectiveness of metaheuristic optimization in enhancing deep learning models for medical diagnosis and provide a robust, scalable framework for AI-driven heart disease prediction.
Keywords : Simüle Edilmiş Tavlama, Konvolüsyonlu Sinirsel Ağlar, Kalp Hastalığı Tahmini

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