Classification of CAD Dataset by Using Principal Component Analysis and Machine Learning Approaches

CÜVİTOĞLU A., IŞIK Z.

5th International Conference on Electrical and Electronics Engineering (ICEEE), İstanbul, Türkiye, 3 - 05 Mayıs 2018, ss.340-343 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/iceee2.2018.8391358
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.340-343
  • Anahtar Kelimeler: machine-learning methods, principal component analysis, coronary artery disease, Z-alizadeh sani dataset, DIAGNOSIS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Machine-Learning (ML) methods are applied to diagnose diseases and to observe disease developments. We utilized several ML methods on Z-Alizadeh Sani dataset, which is about Coronary Artery Disease (CAD). We applied t-test for feature selection and then Principal Component Analysis (PCA) to reduce dimensionality because of small sample size. 10-fold Cross-Validation was applied to ML methods, which achieved higher than 80% average accuracy. Besides, sensitivity and specificity results are around 70% and 90%, respectively. The Artificial Neural Network reached 93% AUC, which is the best performance out of six methods. The overall results are quite promising compared to the previous study.