OPTIMIZING CONVOLUTIONAL NEURAL NETWORKS WITH SIMULATED ANNEALING FOR HEART DISEASE PREDICTION

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

• Simulated Annealing
• Convolutional Neural Networks
• Heart Disease Prediction

KALP HASTALIĞI TAHMİNİ İÇİN SİMÜLE EDİLMİŞ TAVLAMA İLE EVRİŞİMSEL SİNİR AĞLARININ OPTİMİZE EDİLMESİ

Abstract

Kardiyovasküler hastalıklar (KVH'ler) dünya çapında önde gelen ölüm nedeni olmaya devam etmekte ve erken tanı ve etkili tedaviyi destekleyebilecek güvenilir tahmin modellerine acil ihtiyaç olduğunu vurgulamaktadır. Bu çalışma, filtre sayısı, çekirdek boyutu, gizli birimler ve parti boyutu dahil olmak üzere kritik hiperparametreleri optimize etmek için Evrişimli Sinir Ağları'nı (CNN'ler) Simüle Edilmiş Tavlama (SA) algoritmasıyla birleştiren yeni bir çerçeve sunmaktadır. Deneyler, 14 klinik özelliğe sahip 303 hasta kaydı içeren UCI Makine Öğrenmesi Deposu'ndan halka açık Cleveland Kalp Hastalığı veri kümesi üzerinde yürütülmüştür. Önerilen SA-CNN modeli, %96,1'lik bir doğruluk ve 0,96'lık bir F1 puanı elde ederek, temel CNN'leri ve ızgara araması ve rastgele arama gibi geleneksel optimizasyon tekniklerini geride bırakmıştır. SA algoritması, hiperparametre alanında sistematik olarak gezinerek aşırı uyumu azaltmış ve modelin genelleme yeteneğini geliştirmiştir. Bu bulgular, tıbbi teşhis için derin öğrenme modellerini geliştirmede metasezgisel optimizasyonun etkinliğini vurgulamakta ve yapay zeka destekli kalp hastalığı tahmini için sağlam, ölçeklenebilir bir çerçeve sağlamaktadır.

Keywords

• Simüle Edilmiş Tavlama
• Konvolüsyonlu Sinirsel Ağlar
• Kalp Hastalığı Tahmini

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