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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 41 Sayı: 1

Kalp hastalığı tahmin performansının değerlendirilmesinde özellik seçimi yöntemlerine ve makine öğrenmesi algoritmalarına dayalı karşılaştırmalı bir analiz

Özlem Akarçay Pervin(KTO KARATAY ÜNİVERSİTESİ, TİCARET VE SANAYİ MESLEK YÜKSEKOKULU)0000-0003-0068-3211
Nimet Yapıcı Pehlivan(SELÇUK ÜNİVERSİTESİ, FEN FAKÜLTESİ)0000-0002-7094-8097
Gerhard-wilhelm Weber(ORTA DOĞU TEKNİK ÜNİVERSİTESİ, UYGULAMALI MATEMATİK ENSTİTÜSÜ, UYGULAMALI MATEMATİK ANABİLİM DALI (DİSİPLİNLERARASI))0000-0003-0849-7771
Pages
253–270
Publication Date
2026-02-20
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AI Summary & Key Takeaways

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

According to the results, the highest accuracy (0.90), precision (0.89) and recall (0.90) are computed from the RF and LARS+KNN with SMOTE.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Heart disease has become one of the leading causes of death worldwide in recent years.
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Key Findings & Data:The classification performance of ML algorithms can be adversely affected by class imbalance and the presence of a large number of features in the dataset.
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Conclusion & Impact:According to the results, the highest accuracy (0.90), precision (0.89) and recall (0.90) are computed from the RF and LARS+KNN with SMOTE.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kalp hastalığı · SMOTE yeniden örnekleme · özellik seçimi

Abstract

Heart disease has become one of the leading causes of death worldwide in recent years. In this study, ML algorithms including KNN, SVM, DT, RF, and LR were employed to predict heart disease status. The classification performance of ML algorithms can be adversely affected by class imbalance and the presence of a large number of features in the dataset. Therefore, the SMOTE was applied to balance the dataset. To identify relevant features, feature selection methods including LASSO, ElasticNet, and LARS were utilized. Classification performance was evaluated using accuracy, precision, recall, F1-score, MCC, n-MCC, and ROC-AUC. Comparative analyses were conducted on a real-world dataset with and without the application of SMOTE and feature selection methods. According to the results, the highest accuracy (0.90), precision (0.89) and recall (0.90) are computed from the RF and LARS+KNN with SMOTE. The highest F1-score (0.90) is handled by the RF model with SMOTE The highest n-MCC (0.94) and ROC-AUC (0.98) are obtained from the LARS+KNN with SMOTE. It has been observed that the performance of all ML algorithms considered in the study increases significantly when SMOTE is used to address the class imbalance problem and feature selection methods are employed to eliminate irrelevant features.

Özet

Kalp hastalığı, son yıllarda dünya genelinde başlıca ölüm nedenlerinden biri olmuştur. Bu çalışmada, kalp hastalığı durumunu tahmin etmek amacıyla K-en yakın komşuluk (KNN), Destek Vektör Makinesi (SVM), Karar Ağacı (DT), Rastgele Orman (RF) ve Lojistik Regresyon (LR) gibi makine öğrenmesi algoritmaları kullanılmıştır. Makine öğrenmesi (ML) algoritmalarının sınıflandırma performansı, veri kümesinin çok sayıda özellik içermesinden ve sınıf dengesizliğinden etkilenebilmektedir. Bu nedenle, veri kümesini dengelemek amacıyla özellik seçimi yöntemlerinden Sentetik Azınlık Örnekleme Tekniği (SMOTE) uygulanmıştır. İlgili özellikleri belirlemek amacıyla En Küçük Mutlak Büzülme ve Seçim Operatörü (LASSO), ElasticNet ve En Küçük Açılı Regresyon (LARS) kullanılmıştır. Sınıflandırma performansları doğruluk, kesinlik, duyarlılık, F1-skoru, Matthew korelasyon katsayısı (MCC), normalize-MCC (n-MCC) ve ROC-AUC metrikleri ile değerlendirilmiştir. Karşılaştırmalı analizler, gerçek bir veri kümesi üzerinde SMOTE ve özellik seçimi yöntemleri uygulanmadan ve uygulanarak gerçekleştirilmiştir. Sonuçlara göre en yüksek doğruluk (0,90), kesinlik (0,89) ve duyarlılık (0,90) değerleri SMOTE uygulanmış RF ve LARS+KNN’den, en yüksek F1-skoru (0,90) değeri SMOTE uygulanmış RF’den, en yüksek n-MCC (0,94) ve ROC-AUC (0,98) değerleri ise SMOTE uygulanmış LARS+KNN’den elde edilmiştir. Genel olarak, SMOTE ve özellik seçimi yöntemlerinin kullanılması durumunda tüm ML algoritmalarının performansının önemli ölçüde arttığı görülmüştür.

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Cite This Article

Pervin, Ö. A., Pehlivan, N. Y., Weber, G. (2026). Kalp hastalığı tahmin performansının değerlendirilmesinde özellik seçimi yöntemlerine ve makine öğrenmesi algoritmalarına dayalı karşılaştırmalı bir analiz. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 41 Sayı: 1, 253–270. https://doi.org/10.17341/gazimmfd.1513919

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