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International Journal of Clinical Medicine and Bioengineering
ISSN:2737-534X
Frequency: Quarterly Published by lIKll


Open Access Research Paper
 IJCMB 2022/06
Vol.2, Iss.2 : 35-39
https://doi.org/10.35745/ijcmb2022v02.02.0004

Building Statistical Model for Predicting Risk of Diabetes


Te-Jen Su1, Feng-Chun Lee1 and Shih-Ming Wang2*


1Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung, Taiwan
2Cheng Shiu University, Kaohsiung, Taiwan, Department of Computer Science and Information Engineering, Kaohsiung, Taiwan


Abstract:
In recent years, diabetes has become one of the most common human diseases in the world, and is even the main cause of high mortality and economic losses, while timely diagnosis and prediction provide patients with appropriate methods for prevention and treatment. By using a logistic regression model, we tried to predict type 2 diabetes. The statistical analysis was conducted with SPSS for descriptive analysis of data, a chi-square test, and logistic regression analysis to predict the risk factor of diabetes. As the result, five main predictive factors were identified: waist circumference, family history, hypertension, cardiovascular disease, and age. The overall prediction rate of the logistic regression model for predicting diabetes was 80%. The research results help prevent the occurrence of diabetes or facilitate early treatment, reduce misdiagnosis and avoid wasting health care resources.

Keywords:  Type 2 Diabetes, Risk factors, Logistic Regression

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Received:April 19, 2022; Revised:May 10, 2022; Accepted:May 19, 2022; Published:June 30, 2022
*Corresponding author; e-mail: K1115@gcloud.csu.edu.tw


Citation:Su, T.J.; Lee, F.C.; Wang, S.M.Building Statistical Model for Predicting Risk of Diabetes. International Journal of Clinical Medicine and Bioengineering 2022, 2, 35-39. https://doi.org/10.35745/ijcmb2022v02.02.0004

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Copyright: © 2022  The Author(s). Published with license by IIKII, Singapore. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

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