Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease - Open Journal of Statistics

OJS > Vol.10 No.4, August 2020

Open Journal of Statistics

Volume 10, Issue 4 (August 2020)

ISSN Print: 2161-718X ISSN Online: 2161-7198

Google-based Impact Factor: 0.53 Citations

Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease ()

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Download as PDF (Size: 540KB) PP. 694-705

DOI: 10.4236/ojs.2020.104043 522 Downloads 1,982 Views Citations

Author(s)

Keshab R. Dahal^1*, Yadu Gautam²

Affiliation(s)

¹Department of Statistics, Truman State University, Kirksville, MO, USA.
²Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA.

ABSTRACT

Cardiovascular disease (CVD) is a leading cause of death across the globe. Approximately 17.9 million of people die globally each year due to CVD, which comprises 31% of all death. Coronary Artery Disease (CAD) is a common type of CVD and is considered fatal. Predictive models that use machine learning algorithms may assist health workers in timely detection of CAD which ultimately reduces the mortality. The main purpose of this study is to build a predictive model that provides doctors and health care providers with personalized information to implement better and more personalized treatments for their patients. In this study, we use the publicly available Z-Alizadeh Sani dataset which contains random samples of 216 cases with CAD and 87 normal controls with 56 different features. The binary variable “Cath” which represents case-control status, is used the target variable. We study its relationship with other predictors and develop classification models using the five different supervised classification machine learning algorithms: Logistic Regression (LR), Classification Tree with Bagging (Bagging CART), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). These five classification models are used to investigate the detection of CAD. Finally, the performance of the machine learning algorithms is compared, and the best model is selected. Our results indicate that the SVM model is able to predict the presence of CAD more effectively and accurately than other models with an accuracy of 0.8947, sensitivity of 0.9434, specificity of 0.7826, and AUC of 0.8868.

KEYWORDS

Machine Learning, Classification Model Comparison, Coronary Artery Disease, Data Mining

Share and Cite:

Dahal, K. and Gautam, Y. (2020) Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease. Open Journal of Statistics, 10, 694-705. doi: 10.4236/ojs.2020.104043.

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