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Journals / Medical Records / 2026 / Cilt: 4 Sayı: 1

Early Detection of Coronary Heart Disease Based on Machine Learning Methods

Rüstem Yılmaz(SAMSUN GAZİ DEVLET HASTAHANESİ)0000-0003-0587-3356
Fatma Hilal Yağın(İNÖNÜ ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0002-9848-7958
Pages
1–6
Publication Date
2022-01-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We think that this result, which has a high sensitivity criterion in order to minimize overlooked heart patients, is clinically very important.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Heart disease detection using machine learning methods has been an outstanding research topic as heart diseases continue to be a burden on healthcare systems around the world.
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Key Findings & Data:Therefore, in this study, the performances of machine learning methods for predictive classification of coronary heart disease were compared.
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Conclusion & Impact:We think that this result, which has a high sensitivity criterion in order to minimize overlooked heart patients, is clinically very important.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Heart Disease · machine learning · classification

Abstract

Aim: Heart disease detection using machine learning methods has been an outstanding research topic as heart diseases continue to be a burden on healthcare systems around the world. Therefore, in this study, the performances of machine learning methods for predictive classification of coronary heart disease were compared. Material and Method: In the study, three different models were created with Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms for the classification of coronary heart disease. For hyper parameter optimization, 3-repeats 10-fold repeated cross validation method was used. The performance of the models was evaluated based on Accuracy, F1 Score, Specificity, Sensitivity, Positive Predictive Value, Negative Predictive Value, and Confusion Matrix (Classification matrix). Results: RF 0.929, SVM 0.897 and LR 0.861 classified coronary heart disease with accuracy. Specificity, Sensitivity, F1-score, Negative predictive and Positive predictive values of the RF model were calculated as 0.929, 0.928, 0.928, 0.929 and 0.928, respectively. The Sensitivity value of the SVM model was higher compared to the RF. Conclusion: Considering the accurate classification rates of Coronary Heart disease, the RF model outperformed the SVM and LR models. Also, the RF model had the highest sensitivity value. We think that this result, which has a high sensitivity criterion in order to minimize overlooked heart patients, is clinically very important.

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Cite This Article

Yılmaz, R., Yağın, F. H. (2022). Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical Records, Cilt: 4 Sayı: 1, 1–6. https://doi.org/10.37990/medr.1011924

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