TY - JOUR
T1 - Machine Learning based Predictors of Cardiovascular Disease among Young Adults
AU - Shetty, Dasharathraj K.
AU - Rodrigues, Lewlyn Lester Raj
AU - Shetty, Ajith Kumar
AU - Nair, Girish
N1 - Funding Information:
Dr. Girish Nair received his Ph.D. in Management in the areas of Financial Accounting and Industrial Economics and Master’s Degree in Commerce, International Business and Business Administration. More than 18 years of teaching, research, and administrative experience with several international publications to his credit in the areas of Corporate Finance and Financial Economics and Hospitality Management. Presented his research and acted as a chair for technical sessions at various conferences. He is also an International Examiner for Ph.D. research at various universities in India. He is one of the Principal Investigators on NPRP research projects funded by Qatar National Research Fund
Publisher Copyright:
© Engineered Science Publisher LLC 2022
PY - 2022
Y1 - 2022
N2 - The purpose of this research was to develop a data-driven model to test the association of physical, metabolic, and hemodynamic variables on the risk of cardiovascular disease. The structural equation modelling using partial least square method has been adopted to analyze the data. A sample size of 685 young adults who were in sedentary, physically trained, and endurance tested categories has been used in this research. Results have revealed that age and weight were the prominent predictors of the cardiovascular disease among the physical variables, total glucose and triglycerides were the prominent predictors among the metabolic variables, and systemic vascular resistance and systolic blood pressure were the prominent predictors of the cardiovascular disease among the hemodynamic variables. It was concluded that while all the three variables which are considered to be the antecedents of risk of cardiovascular disease, not all the parameters listed under these three categories have a statistically significant influence on the risk of the cardiovascular disease. The results can be of use to the medical practitioners as well as researchers in machine learning, as it adds to the repository of earlier studies and can be used by the medical professionals in effective decision making in disease prediction.
AB - The purpose of this research was to develop a data-driven model to test the association of physical, metabolic, and hemodynamic variables on the risk of cardiovascular disease. The structural equation modelling using partial least square method has been adopted to analyze the data. A sample size of 685 young adults who were in sedentary, physically trained, and endurance tested categories has been used in this research. Results have revealed that age and weight were the prominent predictors of the cardiovascular disease among the physical variables, total glucose and triglycerides were the prominent predictors among the metabolic variables, and systemic vascular resistance and systolic blood pressure were the prominent predictors of the cardiovascular disease among the hemodynamic variables. It was concluded that while all the three variables which are considered to be the antecedents of risk of cardiovascular disease, not all the parameters listed under these three categories have a statistically significant influence on the risk of the cardiovascular disease. The results can be of use to the medical practitioners as well as researchers in machine learning, as it adds to the repository of earlier studies and can be used by the medical professionals in effective decision making in disease prediction.
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U2 - 10.30919/es8d627
DO - 10.30919/es8d627
M3 - Article
AN - SCOPUS:85124613607
SN - 2576-988X
VL - 17
SP - 292
EP - 302
JO - Engineered Science
JF - Engineered Science
ER -
