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, Turkey, 3 - 05 May 2018, pp.340-343 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/iceee2.2018.8391358
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.340-343
  • Keywords: machine-learning methods, principal component analysis, coronary artery disease, Z-alizadeh sani dataset, DIAGNOSIS
  • Dokuz Eylül University Affiliated: Yes

Abstract

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.