Heart Attack Classification with a Machine Learning Approach Based on the Random Forest Algorithm

Abstract

Heart attack diagnosis delays constitute a critical health problem that increases the risk of mortality. Timely and accurate identification of cardiac events is therefore essential to improve patient outcomes and reduce preventable deaths. This study aims to develop a random forest based classification model using the Heart Disease Classification dataset published on the Kaggle platform to support early diagnosis. This dataset consists of 1319 samples and 8 demographic, clinical and biochemical features for the diagnosis of heart disease. To evaluate the model’s reliability and generalizability, a 10-fold cross-validation technique was employed. Through this method, each data instance contributed to both training and testing phases, enabling a more stable and robust performance assessment. This approach also reduced the risk of overfitting and ensured more representative evaluation metrics. The performance of the model was evaluated with ROC curve, training-validation curves, confusion matrix. In the evaluation process, especially in Fold 6, 100% accuracy, precision, recall and F1 score were obtained and it was revealed that the model showed superior performance in the classification task. In addition, as a result of the feature importance analysis, it was determined that troponin, potassium (kcm) and age variables came to the forefront in the decision process. This study aims to fill an important gap in the literature in terms of both strong classification performance and interpretability in the field of machine learning models for heart attack diagnosis.

Keywords

• Heart Attack Classification
• Machine Learning
• Random Forest Algorithm
• Clinical Decision Support Systems

Random Forest Algoritmasına Dayalı Makine Öğrenmesi Yaklaşımıyla Kalp Krizi Sınıflandırması

Abstract

Kalp krizi tanısındaki gecikmeler, mortalite riskini artıran kritik bir sağlık sorunu oluşturmaktadır. Bu nedenle, kardiyak olayların zamanında ve doğru bir şekilde tanımlanması, hasta sonuçlarını iyileştirmek ve önlenebilir ölümleri azaltmak açısından büyük önem taşımaktadır. Bu çalışma, erken tanıyı desteklemek amacıyla Kaggle platformunda yayımlanan Kalp Hastalığı Sınıflandırma veri seti kullanılarak Random Forest tabanlı bir sınıflandırma modeli geliştirmeyi amaçlamaktadır. Bu veri seti, kalp hastalığı tanısı için 1319 örneklem ve 8 demografik, klinik ve biyokimyasal özelliği içermektedir. Modelin güvenilirliğini ve genellenebilirliğini değerlendirmek için 10 katlı çapraz doğrulama yöntemi kullanılmıştır. Bu yöntem sayesinde her bir veri örneği hem eğitim hem de test aşamalarına katkı sağlamış, böylece daha kararlı ve sağlam bir performans değerlendirmesi yapılmıştır. Aynı zamanda bu yaklaşım, aşırı öğrenme riskini azaltmış ve daha temsil edici değerlendirme metrikleri elde edilmesini sağlamıştır. Modelin performansı ROC eğrisi, eğitim-doğrulama eğrileri ve karışıklık matrisi ile değerlendirilmiştir. Değerlendirme sürecinde özellikle 6. katmanda %100 doğruluk, kesinlik, duyarlılık ve F1 skoru elde edilmiş; modelin sınıflandırma görevinde üstün performans sergilediği ortaya konmuştur. Ayrıca, özellik önem düzeyi analizi sonucunda troponin, potasyum (kcm) ve yaş değişkenlerinin karar verme sürecinde öne çıktığı belirlenmiştir. Bu çalışma, kalp krizi tanısına yönelik makine öğrenmesi modelleri alanında hem güçlü sınıflandırma performansı hem de yorumlanabilirlik açısından literatürde önemli bir boşluğu doldurmayı hedeflemektedir.

Keywords

• Kalp Krizi Sınıflandırması
• Makine Öğrenmesi
• Random Forest Algoritması
• Klinik Karar Destek Sistemleri

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