Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Parastoo Golpour, Majid Ghayour-Mobarhan, Azadeh Saki, Habibollah Esmaily, Ali Taghipour, Mohammad Tajfard, Hamideh Ghazizadeh, Mohsen Moohebati and Gordon A. Ferns
Additional contact information
Parastoo Golpour: Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Majid Ghayour-Mobarhan: International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Azadeh Saki: Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Habibollah Esmaily: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Ali Taghipour: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Mohammad Tajfard: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Hamideh Ghazizadeh: International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Mohsen Moohebati: Cardiovascular Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran
Gordon A. Ferns: Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK

IJERPH, 2020, vol. 17, issue 18, 1-9

Abstract: (1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

Keywords: logistic regression; support vector machine; naïve Bayes; angiography (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/18/6449/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/18/6449/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:18:p:6449-:d:408963

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().