Prevalence and Risk Factors of Type 2 Diabetes According to Gender among Korean Employees

Kim, Sang-A; Park, Woong‐Sub; Yu, Su Jeong; Chae, Young Ran; Choi, D.

doi:10.5762/kais.2015.16.11.7589

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…In case of cardiovascular disease, systolic blood pressure (0.164) and high-density lipoprotein (0.113) had the second highest influence on disease occurrence. These results are consistent with the results of previous studies that studied the risk factors of the 4 chronic diseases [16,17].…”

Section: Shapley Additive Explanations Model Variable Importancesupporting

confidence: 93%

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Chronic Disease Prediction Using the Common Data Model: Development Study

Lee¹,

Jo²,

Woo³

et al. 2022

JMIR AI

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Background Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. Objective This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. Methods In this study, 4 major chronic diseases—namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease—were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. Results For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model’s performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. Conclusions This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data–based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.

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Section: Shapley Additive Explanations Model Variable Importancesupporting

confidence: 93%

“…In the case of type 2 diabetes, fasting blood glucose (Shapley value=1.895), age (1.271), and BMI (0.245) influenced the occurrence of diabetes within 10 years [ 16 ]. For hypertension, hyperlipidemia (1.272), cardiovascular disease (1.379), and age (1.117) had the greatest influence on disease occurrence.…”

Section: Resultsmentioning

confidence: 99%

Chronic Disease Prediction Using the Common Data Model: Development Study

Lee¹,

Jo²,

Woo³

et al. 2022

JMIR AI

View full text Add to dashboard Cite

show abstract

Factors affecting glycated hemoglobin(HbA1c) checkup of diabetic patients by gender: Using the community health survey

Jeong¹,

Hwang²

2019

kjhep

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The Associated Factors of Medical Treatment in Diabetic Patients

Choi¹,

Ryu²,

Park³

et al. 2021

J Health Info Stat

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Objectives: The purpose of this study was to identify related factors related to the treatment of diabetic patients.Methods: The subjects of this study were 23,772 diabetic patients aged 30 years and older who participated in the 2016 Community Health Survey. The independent variables were sociodemographic factors, lifestyle and health-related characteristics and diabetes-related characteristics and the dependent variables were related to diabetes treatment. The statistical analysis methods used to find factors related to treatment in diabetic patients were the chi-square test and multiple logistic regression analysis.Results: The drug treatment rate of diabetic patients was 88.7%. Among sociodemographic characteristics, drug treatment for diabetes was significantly related to women, increasing age, higher educational level and living in rural area. Among the characteristics related to lifestyle and health status, walking practice and subjective health status were significantly related and among diabetes related characteristics, there were significant correlations in whether it was blood glucose level, self-cognitive blood glucose control, and diabetes management education.Conclusions: Conclusively, gender, age, education level, residential area, walking practice, self-rated health and knowledge of DM were associated with treatment of DM. An effective diabetes management program that reflects the relevant factors should be applied to the treatment of DM.

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Prevalence and Risk Factors of Type 2 Diabetes According to Gender among Korean Employees

Cited by 4 publications

References 13 publications

Chronic Disease Prediction Using the Common Data Model: Development Study

Chronic Disease Prediction Using the Common Data Model: Development Study

Factors affecting glycated hemoglobin(HbA1c) checkup of diabetic patients by gender: Using the community health survey

The Associated Factors of Medical Treatment in Diabetic Patients

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