Analysis of Artificial Intelligence Methods in Classifying Heart Attack Risk: Black-Box Models vs. Glass-Box Models

Yıl 2025, Cilt: 37 Sayı: UYIK 2024 Special Issue, 65 - 76

Ebru Geçici , Eyüp Ensar Işık , Mısra Şimşir , Mehmet Güneş

https://doi.org/10.7240/jeps.1506705

Öz

Artificial Intelligence (AI) is becoming more and more involved in human life day by day. Healthcare is one of the areas where AI is widely used, such as in the diagnosis prediction, and/or classification of diseases. Techniques such as machine learning provide high-accuracy results, but many algorithms have black-box structures, where the reasoning behind the predictions is not known. Explainable AI emerges to address this by providing explanations for complex models. While interpretable ("glass-box") models are desirable, they may have lower accuracy than complex ("black-box") models. Finding the right balance is crucial, especially in critical areas such as healthcare. It is also important to provide individual explanations for the predictions. This study uses patient data to explore a model to predict heart attack risk. Therefore, we compare glass-box models (logistic regression, naive Bayes, decision tree, and explainable boosting) with black-box models (random forest, support vector machine, multi-layer perceptron, gradient boosting, and stochastic gradient boosting). The results show that explainable boosting achieves the highest accuracy. To delve into individual explanations on a patient basis, the explainable boosting algorithm is compared with the random forest algorithm, which gives the best results among the black-box models. Here, LIME and SHAP are used to provide interpretability of random forests. As a result, it is concluded that the random forest algorithm has differences in the importance weights of the variables compared to the explainable boosting algorithm. Both results provide valuable tools for healthcare stakeholders to choose the most appropriate model.

Anahtar Kelimeler

Artificial Learning, Explainable Artificial Intelligence, Classification, Healthcare Industry, Heart Attack

Kalp Krizi Riskinin Sınıflandırılmasında Yapay Zekâ Yöntemlerinin Analizi: Kara Kutu Modelleri ve Cam Kutu Modelleri

Öz

Yapay Zekâ (YZ) her geçen gün insan hayatına giderek daha fazla dâhil olmaktadır. Sağlık sektörü, hastalıkların teşhisi, tahmini ve/veya sınıflandırılması gibi yapay zekânın yaygın olarak kullanıldığı alanlardan biridir. Makine öğrenimi gibi teknikler yüksek doğrulukta sonuçlar sağlar ancak çoğu algoritma, tahminlerin ardındaki mantığın bilinmediği kara kutulardır. Açıklanabilir Yapay Zekâ, karmaşık modeller için açıklamalar sağlayarak bu sorunu çözmek üzere ortaya çıkmaktadır. Yorumlanabilir ("cam kutu") modeller tercih edilmekle birlikte, karmaşık ("kara kutu") modellerden daha düşük doğruluğa sahip olabilirler. Özellikle sağlık gibi kritik alanlarda doğru dengeyi bulmak çok önemlidir. Ayrıca tahminlerin bireysel olarak açıklanması da büyük önem taşımaktadır. Bu çalışmada hasta verilerine dayanarak kalp krizi riskini tahmin etmeye yönelik bir model araştırıyoruz. Bu amaçla, cam kutu modellerini (lojistik regresyon, naif Bayes, karar ağacı ve açıklanabilir artıma) kara kutu modelleriyle (rastgele orman, destek vektör makinesi, çok katmanlı algılayıcılar ve gradyan artırma) karşılaştırıyoruz. Sonuçlar açıklanabilir güçlendirmenin en yüksek doğruluğu sağladığını göstermektedir. Hasta bazında bireysel açıklamalara girebilmek için açıklanabilir artırma algoritması, kara kutu modelleri arasında en iyi sonuçları veren rastgele orman algoritması ile karşılaştırılmıştır. Burada rastgele ormanların yorumlanabilirliğini sağlamak için LIME ve SHAP teknikleri kullanılmıştır. Sonuç olarak rastgele orman algoritmasının açıklanabilir artırma algoritmasına göre değişkenlerin önem ağırlıklarında farklılıklar olduğu sonucuna varılmıştır. Her iki sonuç da sağlık hizmeti paydaşlarının en uygun modeli seçmeleri için değerli araçlar sunmaktadır.

Anahtar Kelimeler

Yapay Öğrenme, Açıklanabilir Yapay Zekâ, Sınıflandırma, Sağlık Sektörü, Kalp Krizi

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