Regression analysis for potential indicators of diabetes

Yiqi Zhou

doi:10.1117/12.2669392

24 March 2023 Regression analysis for potential indicators of diabetes

Yiqi Zhou

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 1261158 (2023) https://doi.org/10.1117/12.2669392
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

Diabetes, as one of the common chronic diseases in the world, negatively affects millions of people's life quality. Most of the research examined the relationship between diabetes and well-known health issues (e.g., high blood pressure and BMI level). However, research that evaluates the relationship among diabetes and health-related lifestyles is limited. This research aims to examine the association between diabetes and each of the potential indicators including health-related lifestyles. The dataset of this study is obtained from the BRFSS survey in 2015 collected by CDC, where 70692 participants selected in this study. Binary logistic regression is adopted to evaluate the association between diabetes and each of the indicators adjusted by sex, age, education level, and income level. High blood pressure and high cholesterol are considered as two strongest indicators of diabetes, the risk of diabetes increases as the BMI level increases. In addition, smoking status plays as a weak indicator of diabetes. For health-related lifestyles, the result showed that increased consume of fruits, vegetables, and exercise are associated with decreased risk of diabetes. People with "heavy drinker" showed a half chance less likely to have diabetes than others. These results shed light on predicting the potential risk of diabetes and providing effective ways to prevent diabetes.

Citation Download Citation

Yiqi Zhou "Regression analysis for potential indicators of diabetes", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 1261158 (24 March 2023); https://doi.org/10.1117/12.2669392

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KEYWORDS

Blood pressure

Binary data

Diseases and disorders

Correlation coefficients

Analytical research

Cardiovascular disorders

Data analysis

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