Application of Quantile Regression and Ordinary Least Squares Regression in Modeling Body Mass Index in Federal Medical Centre Jalingo, Nigeria
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552-558
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10.58578/mikailalsys.v3i2.5322
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Authors:Adeniyi Oyewole Ogunmola1*, Benjamin Ekene Okoye2
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Abstract
Body mass index is a measure of nutritional status of an individual. Malnutrition is a leading public health problem in developing countries like Nigeria, it is also a major cause of morbidity and mortality. In this study, Body mass index is modeled using ordinary least squares method and quantile regression method. Data is collected from Antiretroviral therapy Clinic in Federal Medical Centre, Jalingo. Variables in the data collected are the Body mass index, age, weight, height, sex and occupation of the patients. Results showed that the ordinary least square regression and quantile regression at 25th percentile, median percentile, 75th percentile and 95th percentile fit the data. Weight, age, sex and height of patients are significant in determining the BMI of the patients when OLS method is applied. While weight, sex and height of patients are significant in determining the BMI of the patients. It is also discovered that OLS method fits the data more than quantile regression method using AIC and MSE.
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References
Adedia D., Boakye A.A., Mensah D., Lokpo S.Y., Afeke I., and Duedu K.O. (2020). Comparative assessment of anthropometric and bioimpedence methods for determining adiposity. Heliyon, Volume 6, Issue 12, e05740, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2020.e05740. (https://www.sciencedirect.com/science/article/pii/S2405844020325834)
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261-304.
Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.
Mohajan D. and Mohajan H.K. (2023). Body Mass Index (BMI) is a Popular Anthropometric Tool to Measure Obesity Among Adults. Paradigm Academic Press Journal of Innovations in Medical Research ISSN 2788-7022 APR. 2023 VOL.2, NO.4. doi:10.56397/JIMR/2023.04.06
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). John Wiley & Sons.
National Heart, Lung, and Blood Issue (NIH) (2024). Assessing Your Weight and Health Risk. Assessing Your Weight and Health Risk (nih.gov) September, 15th 2024.
Safaei M., Sundararajan E.A., Driss M., Boulila W., Shapi’I A. (2021). Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Computers in Biology and Medicine, Volume 136, 2021, 104754, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2021.104754. (https://www.sciencedirect.com/science/article/pii/S0010482521005485)
World Health Organsation (WHOa) (2024). Health Topics Obesity. https://www.who.int/health-topics/obesity September, 15th 2024.
World Health Organsation (WHOb) (2024). Health Topics Diabetics. https://www.who.int/health-topics/diabetics September, 15th 2024.