A MULTIPLE REGRESSION MODEL FOR IDENTIFYING SOME RISK FACTORS AFFECTING THE CARDIOVASCULAR HEALTH ISSUES IN ADULTS
By
Mohammad Shakil1, Mohammad Ahsanullah2, B. M. G. Kibria3, J. N. Singh4, Rakhshinda
Jabeen5, Aneeqa Khadim6 and Musaddiq Sirajo7
1Department of Mathematics, Miami Dade College, Hialeah, FL, USA
2Department of Management Sciences, Professor Emeritus, Rider University, NJ, USA
3Department of Mathematics & Statistics, Florida International University, Miami, FL, USA
4Department of Mathematics & Computer Sciences, Barry University, Miami Shores, FL, USA
5Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
6Department of Mathematics, Mirpur University of Science & Technology, Mirpur, Pakistan
7Department of Statistics, Ahmadu Bello University, Zaria, Nigeria
Email: mshakil@mdc.edu,ahsan@rider.edu,kibriag@u.edu, jsingh@barry.edu,
rakhshinda.jabeen@duhs.edu.pk,Aneeqa89@gmail.com,musaddi,musaddiqsirajo@gmail.com
(Received: June 20, 2023; In format: August 26, 2023, Revised: September 29, 2023; Accepted: October 03, 2023)
DOI: https://doi.org/10.58250/jnanabha.2023.53214
Abstract
Multiple Regression analysis is one of the most critical and widely used statistical techniques in medical and applied research. It is dened as a multivariate technique for determining the correlation between a response variable and some combination of two or more predictor variables. Moreover, it is well-known in medical sciences that the obesity, high blood pressure and high cholesterol are major risk factors for cardiovascular health issues. The body mass index is a measure of body size, and combines a person's weight with their height, and therefore can affect their obesity, high blood pressure, high cholesterol and type 2 diabetes mellitus significantly, which are major risk factors for cardiovascular health issues in adults. Motivated by these facts, in this paper, a multiple linear regression model is developed to analyze the obesity in adults, based on a sample data of adult's age, height, weight, waist, diastolic blood pressure, systolic blood pressure, pulse, cholesterol, and the body mass index measurements. The use of multiple linear regression is illustrated in the prediction study of adult's obesity based on their body mass index. It is observed that in the presence of adult's age, weight, waist, diastolic blood pressure, systolic blood pressure, pulse, and cholesterol levels, height is a good predictor of the body mass index. Moreover, in the presence of age, height, waist, diastolic blood pressure, systolic blood pressure, pulse, and cholesterol levels, weight is a good predictor of the body mass index. Some concluding remarks are given in the end.
2020 Mathematical Sciences Classification: 65F359, 15A12, 15A04, 62J05.
Keywords and Phrases: Cardiovascular, high cholesterol levels, high blood pressure, multiple regression, obesity.