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

Ali Cüvitoǧlu; Zerrin Işik

doi:10.1109/ICEEE2.2018.8391358

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

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-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 language	English
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	https://doi.org/10.1109/ICEEE2.2018.8391358
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
Country/Territory	Turkey
City	Istanbul
Period	3/05/18 → 5/05/18

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10.1109/ICEEE2.2018.8391358

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@inproceedings{be0ca22acf564cc191c768dd080ccbeb,

title = "Classification of CAD dataset by using principal component analysis and machine learning approaches",

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.",

author = "Ali C{\"u}vitoǧlu and Zerrin I{\c s}ik",

year = "2018",

month = jun,

day = "20",

doi = "10.1109/ICEEE2.2018.8391358",

language = "English",

pages = "340--343",

booktitle = "2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "5th International Conference on Electrical and Electronics Engineering, ICEEE 2018 ; Conference date: 03-05-2018 Through 05-05-2018",

}

Cüvitoǧlu, A & Işik, Z 2018, Classification of CAD dataset by using principal component analysis and machine learning approaches. in 2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018. Institute of Electrical and Electronics Engineers Inc., pp. 340-343, 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018, Istanbul, Turkey, 3/05/18. https://doi.org/10.1109/ICEEE2.2018.8391358

Classification of CAD dataset by using principal component analysis and machine learning approaches. / Cüvitoǧlu, Ali; Işik, Zerrin.
2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 340-343.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Classification of CAD dataset by using principal component analysis and machine learning approaches

AU - Cüvitoǧlu, Ali

AU - Işik, Zerrin

PY - 2018/6/20

Y1 - 2018/6/20

N2 - 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.

AB - 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.

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U2 - 10.1109/ICEEE2.2018.8391358

DO - 10.1109/ICEEE2.2018.8391358

M3 - Conference contribution

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BT - 2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018

Y2 - 3 May 2018 through 5 May 2018

ER -

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

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