Classification of CAD dataset by using principal component analysis and machine learning approaches

Ali Cüvitoǧlu, Zerrin Işik

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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 languageEnglish
Title of host publication2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages340-343
ISBN (Electronic)9781538663929
DOIs
Publication statusPublished - 20 Jun 2018
Externally publishedYes
Event5th International Conference on Electrical and Electronics Engineering, ICEEE 2018 - Istanbul, Turkey
Duration: 3 May 20185 May 2018

Conference

Conference5th International Conference on Electrical and Electronics Engineering, ICEEE 2018
Country/TerritoryTurkey
CityIstanbul
Period3/05/185/05/18

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