Long-Term Prediction of Coronary Artery Disease via Ensemble Machine Learning Algorithms

Şehmus Aslan

Article Details

Long-Term Prediction of Coronary Artery Disease via Ensemble Machine Learning Algorithms

Authors:

Şehmus Aslan

Publisher:

Dicle University

Main Topics:

Makine Öğrenme (Diğer)

Journal:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 4, pp.827-837

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001334538

pISSN:

1309-8640

Keywords:

Abstract:

 Coronary artery disease CAD is the leading cause of death worldwide, necessitating early detection methods that are non-invasive, cost-effective, and reliable. In this study, the effectiveness of various machine learning ML models in predicting CAD was evaluated, with a focus on addressing class imbalance using the Synthetic Minority Oversampling Technique SMOTE. The Framingham CAD dataset was utilized, and SMOTE was applied with different k-values to balance the data, examining the impact on prediction performance. Eight significant featuresage, diaBP, glucose, heart rate, sysBP, totChol, cigsPerDay, and BMIwere determined during preprocessing and used for further analysis. Among the models tested, the StackingC classifier demonstrated superior performance, achieving an accuracy of 95.81, sensitivity of 95.9, specificity of 95.7, and an AUROC of 99.2 for k1. These findings highlight the potential of the StackingC model as a robust tool for CAD prediction, offering a promising non-invasive method for early diagnosis.

 Coronary artery disease CAD is the leading cause of death worldwide, necessitating early detection methods that are non-invasive, cost-effective, and reliable. In this study, the effectiveness of various machine learning ML models in predicting CAD was evaluated, with a focus on addressing class imbalance using the Synthetic Minority Oversampling Technique SMOTE. The Framingham CAD dataset was utilized, and SMOTE was applied with different k-values to balance the data, examining the impact on prediction performance. Eight significant featuresage, diaBP, glucose, heart rate, sysBP, totChol, cigsPerDay, and BMIwere determined during preprocessing and used for further analysis. Among the models tested, the StackingC classifier demonstrated superior performance, achieving an accuracy of 95.81, sensitivity of 95.9, specificity of 95.7, and an AUROC of 99.2 for k1. These findings highlight the potential of the StackingC model as a robust tool for CAD prediction, offering a promising non-invasive method for early diagnosis.

 Coronary artery disease CAD is the leading cause of death worldwide, necessitating early detection methods that are non-invasive, cost-effective, and reliable. In this study, the effectiveness of various machine learning ML models in predicting CAD was evaluated, with a focus on addressing class imbalance using the Synthetic Minority Oversampling Technique SMOTE. The Framingham CAD dataset was utilized, and SMOTE was applied with different k-values to balance the data, examining the impact on prediction performance. Eight significant featuresage, diaBP, glucose, heart rate, sysBP, totChol, cigsPerDay, and BMIwere determined during preprocessing and used for further analysis. Among the models tested, the StackingC classifier demonstrated superior performance, achieving an accuracy of 95.81, sensitivity of 95.9, specificity of 95.7, and an AUROC of 99.2 for k1. These findings highlight the potential of the StackingC model as a robust tool for CAD prediction, offering a promising non-invasive method for early diagnosis.

 Koroner arter hastalığı KAH dünya çapında önde gelen ölüm nedenidir ve bu nedenle invaziv olmayan, maliyet etkin ve güvenilir erken tespit yöntemlerine ihtiyaç duyulmaktadır. Bu çalışmada, KAHnın tahmininde çeşitli makine öğrenimi ML modellerinin etkinliği değerlendirildi ve Sınıf Dengesizliğinin Üstesinden Gelme Yöntemi SMOTE kullanılarak sınıf dengesizliğine odaklanıldı. Framingham KAH veri seti kullanılarak SMOTE, verileri dengelemek için farklı k-değerleri ile uygulandı ve tahmin performansına etkisi incelendi. Ön işleme sırasında belirlenen sekiz önemli özellikyaş, diyastolik kan basıncı diaBP, glukoz, kalp hızı, sistolik kan basıncı sysBP, toplam kolesterol totChol, günlük sigara sayısı cigsPerDay ve vücut kitle indeksi BMIdaha ileri analizler için kullanıldı. Test edilen modeller arasında, StackingC sınıflandırıcısı üstün performans göstererek k1 için 95.81 doğruluk, 95.9 duyarlılık, 95.7 özgüllük ve 99.2 AUROC elde etti. Bu bulgular, StackingC modelinin KAH tahmininde sağlam bir araç olarak potansiyelini vurgulamakta ve erken teşhis için umut verici invaziv olmayan bir yöntem sunmaktadır.

Article Details

Long-Term Prediction of Coronary Artery Disease via Ensemble Machine Learning Algorithms

Authors:

Şehmus Aslan

Publisher:

Dicle University

Main Topics:

Makine Öğrenme (Diğer)

Journal:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 4, pp.827-837

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001334538

pISSN:

1309-8640

Machine learning

coronary artery disease prediction

class imbalance

SMOTE

stackingC classifier.Makine öğrenmesi

koroner arter hastalığı tahmini

sınıf dengesizliği

SMOTE

stackingC sınıflandırıcı

Issues

Article Details

Authors: Şehmus Aslan

Publisher: Dicle University

Main Topics: Makine Öğrenme (Diğer)

Journal: Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 4, pp.827-837

Document Type: Article

Publication Date: 12/2024

Document Language: English

Document ID (Org No.): 2001334538

pISSN: 1309-8640

Issues

Authors:

Şehmus Aslan

Publisher:

Dicle University

Main Topics:

Makine Öğrenme (Diğer)

Journal:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 4, pp.827-837

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001334538

pISSN:

1309-8640