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.
Original language | English |
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Title of host publication | 2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 340-343 |
ISBN (Electronic) | 9781538663929 |
DOIs | |
Publication status | Published - 20 Jun 2018 |
Externally published | Yes |
Event | 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018 - Istanbul, Turkey Duration: 3 May 2018 → 5 May 2018 |
Conference
Conference | 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 3/05/18 → 5/05/18 |