Development and validation of machine learning models to predict prediabetes using dietary intake data in young adults in Korea: a cross-sectional study

• 발행기관: 한국기초간호학회
• 저자명: Myoung-Lyun Heo
• 간행물 정보: 『Journal of Korean Biological Nursing Science』제26권 제4호, 300~310쪽, 전체 11쪽
• 주제분류: 의약학 > 의학일반
• 파일형태: PDF
• 발행일자: 2024.11.30

국문 초록

Purpose: This study aimed to develop and compare machine learning models for predicting prediabetes in young adults in Korea using dietary intake data and to identify the most effective model. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey were used, with 823 participants aged 19-35 years selected after excluding those with missing data. Logistic regression, k-nearest neighbors, and random forest models were applied to predict prediabetes, and the analysis was conducted using the Orange 3.5 program. Five-fold cross-validation was performed to reduce performance variability, and test data were used for final model validation. Results: In the dataset, 14%-15% of participants were classified as having prediabetes. The random forest model showed the highest performance in terms of classification accuracy, harmonic mean of precision and recall, and precision. Logistic regression had the highest performance regarding the model’s ability to distinguish between individuals with and without prediabetes. Age, thiamine intake, and water intake emerged as the most important predictors. Conclusion: This study demonstrated the utility of using dietary intake data to predict prediabetes in young adults. The random forest model provided the highest prediction accuracy, supporting early detection and intervention, which could help to reduce unnecessary treatment. This highlights nurses’ important role in educating patients about lifestyle changes and implementing preventive care. Future studies should incorporate additional factors, such as psychological and lifestyle variables, to improve the model's performance.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

CONCLUSION

REFERENCES

키워드

Prediabetic state Machine learning Diet surveys Young adult