TITLE:
Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease
AUTHORS:
Keshab R. Dahal, Yadu Gautam
KEYWORDS:
Machine Learning, Classification Model Comparison, Coronary Artery Disease, Data Mining
JOURNAL NAME:
Open Journal of Statistics,
Vol.10 No.4,
August
19,
2020
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.