Authors : Nikhil Gupta, Nihar Maheshwari and C.V. Arjun
Abstract: Obesity and its related consequences are some of the most alarming health problems the world is currently facing. Studies have shown that a little more than a fourth of the adults all over the world are overweight and a little more than a tenth are obese. Also, obesity is considered as an independent risk factor for determining the severity of a Coronary Artery Disease (CAD). The aim of this study is to determine the role of metabolic obesity and body mass index in patients with coronary artery diseases. We train a model capable of predicting the GENSINI score which determines the severity of CAD in four health groups, namely Metabolically Healthy and Normal Weight (MHNW), Metabolically Obese Normal Weight (MONW), Metabolically Healthy Obese (MHO) and Metabolically Abnormally Obese (MAO). Several factors like glycated Hemoglobin (HBA1C), triglyceride and insulin resistance are considered. Ridge regression, Lasso regression and neural networks are the mechanisms used to train a model for predicting the GENSINI score based upon health statistics of patients.
Nikhil Gupta, Nihar Maheshwari and C.V. Arjun, 2018. Role of Metabolic Obesity and Body Mass Index in Patients with Coronary Artery Diseases. Journal of Engineering and Applied Sciences, 13: 5512-5516.