Early Detection of Coronary Heart Disease Based on Machine Learning Methods
Yıl: 2022 Cilt: 4 Sayı: 1 Sayfa Aralığı: 1 - 6 Metin Dili: İngilizce DOI: 10.37990/medr.1011924 İndeks Tarihi: 27-09-2022
Early Detection of Coronary Heart Disease Based on Machine Learning Methods
Öz: 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.
Anahtar Kelime: Makine Öğrenme Yöntemlerine Dayali Kroner Kalp Hastaliğinin Erken Tespiti
Öz: Amaç: Kalp hastalıklarının dünya çapında sağlık sistemleri üzerinde bir yük olmaya devam etmesi nedeniyle, makine öğrenme yöntemlerini kullanarak kalp hastalığı tespiti olağanüstü bir araştırma konusu olmuştur. Bu nedenle, bu çalışmada, koroner kalp hastalığının tahmin edici sınıflandırması için makine öğrenme yöntemlerinin performansları karşılaştırılmıştır. Materyal ve Metot: Çalışmada koroner kalp hastalığının sınıflandırılması için Rasgele Orman (RF), Lojistik Regresyon (LR) ve Destek Vektör Makinesi (SVM) algoritmaları ile üç farklı model oluşturulmuştur. Hiperparametre optimizasyonu için 3 tekrarlı 10 katlı tekrarlı çapraz doğrulama yöntemi kullanıldı. Modellerin performansı Doğruluk, F1 Skoru, Seçicilik, Duyarlılık, Pozitif Tahmin Değeri, Negatif Tahmin Değeri ve Karışıklık Matrisi (Sınıflandırma matrisi) temel alınarak değerlendirilmiştir. Bulgular: Koroner kalp hastalığını RF 0.929, SVM 0.897 ve LR 0.861 doğrulukla sınıflandırdı. RF modelinin seçicilik, duyarlılık, F1-skor, negatif tahmin ve pozitif tahmin değerleri sırasıyla 0.929, 0.928, 0.928, 0.929 ve 0.928 olarak hesaplanmıştır. Ek olarak SVM modelinin duyarlılık değeri RF’ye göre daha yüksek çıkmıştır. Sonuç: Koroner Kalp hastalığının doğru sınıflandırma oranları göz önüne alındığında, RF modeli SVM ve LR modellerinden daha iyi performans göstermiştir. Ayrıca RF modeli en yüksek duyarlılık değerine sahipti. Gözden kaçırılan kalp hastalarını en aza indirmek için yüksek bir duyarlılık kriterine sahip olan bu sonucun klinik açıdan oldukça önemli olduğunu düşünmekteyiz.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | Yilmaz R, YAĞIN F (2022). Early Detection of Coronary Heart Disease Based on Machine Learning Methods. , 1 - 6. 10.37990/medr.1011924 |
| Chicago | Yilmaz Rustem,YAĞIN Fatma Hilal Early Detection of Coronary Heart Disease Based on Machine Learning Methods. (2022): 1 - 6. 10.37990/medr.1011924 |
| MLA | Yilmaz Rustem,YAĞIN Fatma Hilal Early Detection of Coronary Heart Disease Based on Machine Learning Methods. , 2022, ss.1 - 6. 10.37990/medr.1011924 |
| AMA | Yilmaz R,YAĞIN F Early Detection of Coronary Heart Disease Based on Machine Learning Methods. . 2022; 1 - 6. 10.37990/medr.1011924 |
| Vancouver | Yilmaz R,YAĞIN F Early Detection of Coronary Heart Disease Based on Machine Learning Methods. . 2022; 1 - 6. 10.37990/medr.1011924 |
| IEEE | Yilmaz R,YAĞIN F "Early Detection of Coronary Heart Disease Based on Machine Learning Methods." , ss.1 - 6, 2022. 10.37990/medr.1011924 |
| ISNAD | Yilmaz, Rustem - YAĞIN, Fatma Hilal. "Early Detection of Coronary Heart Disease Based on Machine Learning Methods". (2022), 1-6. https://doi.org/10.37990/medr.1011924 |
| APA | Yilmaz R, YAĞIN F (2022). Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical records-international medical journal (Online), 4(1), 1 - 6. 10.37990/medr.1011924 |
| Chicago | Yilmaz Rustem,YAĞIN Fatma Hilal Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical records-international medical journal (Online) 4, no.1 (2022): 1 - 6. 10.37990/medr.1011924 |
| MLA | Yilmaz Rustem,YAĞIN Fatma Hilal Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical records-international medical journal (Online), vol.4, no.1, 2022, ss.1 - 6. 10.37990/medr.1011924 |
| AMA | Yilmaz R,YAĞIN F Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical records-international medical journal (Online). 2022; 4(1): 1 - 6. 10.37990/medr.1011924 |
| Vancouver | Yilmaz R,YAĞIN F Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical records-international medical journal (Online). 2022; 4(1): 1 - 6. 10.37990/medr.1011924 |
| IEEE | Yilmaz R,YAĞIN F "Early Detection of Coronary Heart Disease Based on Machine Learning Methods." Medical records-international medical journal (Online), 4, ss.1 - 6, 2022. 10.37990/medr.1011924 |
| ISNAD | Yilmaz, Rustem - YAĞIN, Fatma Hilal. "Early Detection of Coronary Heart Disease Based on Machine Learning Methods". Medical records-international medical journal (Online) 4/1 (2022), 1-6. https://doi.org/10.37990/medr.1011924 |
