Explainable Artificial Intelligence Approach to Heart Attack Risk Prediction

Tülay Turan

Makale detayları

Explainable Artificial Intelligence Approach to Heart Attack Risk Prediction

Yazarlar:

Tülay Turan

Yayıncı:

Giresun University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.1-15

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342300

pISSN:

2564-7377

Anahtar kelimeler:

Özet:

 This study examines the feasibility of explainable artificial intelligence XAI techniques for analyzing and accurately classifying heart attack risks. Given the complexity of heart attack risk factors, traditional machine learning models often do not provide the transparency needed for clinical decision-making. This research addresses this gap by incorporating XAI techniques, specifically SHAP SHapley Additive exPlanations, to reveal model predictions. In this retrospective study, multiple databases were searched, and data on eight risk factors of 1319 patients were obtained. Prediction models have been developed using six different machine learning algorithms for heart attack classification. In heart attack risk classification, the XGBoost eXtreme Gradient Boosting model achieved the best predictive values with 91.28 Accuracy, 90 Precision, 92 Recall, and 91 F1-score. In addition, the model algorithms were evaluated according to AUC, and again, the XGBoost model achieved the best result 0.91. In the Random Forest Feature importance evaluation, troponin was the most critical variable affecting the diagnosis. SHAP graphs showed that troponin 4.19 was the most critical risk factor. This research highlights the potential of XAI to bridge the gap between complex AI models and clinical applicability and suggests that future studies move in a promising direction to refine further and validate AI-powered healthcare solutions.

 This study examines the feasibility of explainable artificial intelligence XAI techniques for analyzing and accurately classifying heart attack risks. Given the complexity of heart attack risk factors, traditional machine learning models often do not provide the transparency needed for clinical decision-making. This research addresses this gap by incorporating XAI techniques, specifically SHAP SHapley Additive exPlanations, to reveal model predictions. In this retrospective study, multiple databases were searched, and data on eight risk factors of 1319 patients were obtained. Prediction models have been developed using six different machine learning algorithms for heart attack classification. In heart attack risk classification, the XGBoost eXtreme Gradient Boosting model achieved the best predictive values with 91.28 Accuracy, 90 Precision, 92 Recall, and 91 F1-score. In addition, the model algorithms were evaluated according to AUC, and again, the XGBoost model achieved the best result 0.91. In the Random Forest Feature importance evaluation, troponin was the most critical variable affecting the diagnosis. SHAP graphs showed that troponin 4.19 was the most critical risk factor. This research highlights the potential of XAI to bridge the gap between complex AI models and clinical applicability and suggests that future studies move in a promising direction to refine further and validate AI-powered healthcare solutions.

 This study examines the feasibility of explainable artificial intelligence XAI techniques for analyzing and accurately classifying heart attack risks. Given the complexity of heart attack risk factors, traditional machine learning models often do not provide the transparency needed for clinical decision-making. This research addresses this gap by incorporating XAI techniques, specifically SHAP SHapley Additive exPlanations, to reveal model predictions. In this retrospective study, multiple databases were searched, and data on eight risk factors of 1319 patients were obtained. Prediction models have been developed using six different machine learning algorithms for heart attack classification. In heart attack risk classification, the XGBoost eXtreme Gradient Boosting model achieved the best predictive values with 91.28 Accuracy, 90 Precision, 92 Recall, and 91 F1-score. In addition, the model algorithms were evaluated according to AUC, and again, the XGBoost model achieved the best result 0.91. In the Random Forest Feature importance evaluation, troponin was the most critical variable affecting the diagnosis. SHAP graphs showed that troponin 4.19 was the most critical risk factor. This research highlights the potential of XAI to bridge the gap between complex AI models and clinical applicability and suggests that future studies move in a promising direction to refine further and validate AI-powered healthcare solutions.

 Bu çalışma, kalp krizi risklerinin analiz edilmesi ve doğru bir şekilde sınıflandırılması için açıklanabilir yapay zeka XAI tekniklerinin uygulanabilirliğini incelemeyi amaçlamaktadır. Kalp krizi risk faktörlerinin karmaşıklığı göz önünde bulundurulduğunda, geleneksel makine öğrenmesi modelleri genellikle klinik karar verme için gerekli olan şeffaflığı sağlamamaktadır. Bu araştırma, model tahminlerini açığa çıkarmak için özellikle SHAP SHapley Additive exPlanations gibi XAI tekniklerini dahil ederek bu boşluğu ele almaktadır. Çalışmada birden fazla veri tabanı taranarak 1319 hastanın 8 risk faktörüne ilişkin veriler elde edilmiştir. Kalp krizi sınıflandırması için altı farklı makine öğrenmesi algoritması kullanılarak tahmin modelleri geliştirilmiştir. Kalp krizi risk sınıflandırmasında XGBoost modeli 91,28 Accuracy, 90 Precision, 92 Recall ve 91 F1-Score ile en iyi tahmin değerlerini elde etmiştir. Ayrıca model algoritmaları AUCa göre değerlendirildiğinde, XGBoost modelinin 0,91 doğruluk değeri ile en iyi sonucu elde edttiği görülmüştür. Random Forest özellik önem değerlendirmesinde değişkenler arasında tanıyı etkileyen en kritik değişkenin troponin olduğu görülmüştür. SHAP grafiklerinde de troponin 4.19 en önemli risk faktörü olduğu görülmüştür. Bu araştırma, XAInın, karmaşık AI modelleri ile klinik uygulanabilirlik arasındaki boşluğu kapatma potansiyelini vurgulamakta ve gelecekteki çalışmaların AI destekli sağlık çözümlerini daha da rafine etmek ve doğrulamak için umut verici bir yönde ilerlemesini önermektedir.

Makale detayları

Explainable Artificial Intelligence Approach to Heart Attack Risk Prediction

Yazarlar:

Tülay Turan

Yayıncı:

Giresun University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.1-15

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342300

pISSN:

2564-7377

Explainable Artificial Intelligence

Heart Attack Risk Prediction

Machine Learning

XGBoost

SHAPAçıklanabilir Yapay Zeka

Kalp Krizi Risk Tahmini

Makine Öğrenmesi

XGBoost

SHAP

Sayı

Makale detayları

Yazarlar: Tülay Turan

Yayıncı: Giresun University

Ana Başlıklar: Yazılım Mühendisliği (Diğer)

Dergi: Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.1-15

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001342300

pISSN: 2564-7377

Sayı

Yazarlar:

Tülay Turan

Yayıncı:

Giresun University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.1-15

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342300

pISSN:

2564-7377