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Development of Various Diabetes Prediction Models Using Machine Learning Techniques
Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim
Diabetes Metab J. 2022;46(4):650-657.   Published online March 11, 2022
DOI: https://doi.org/10.4093/dmj.2021.0115
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  • 6 Web of Science
  • 7 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.
Methods
Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.
Results
The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.
Conclusion
We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

Citations

Citations to this article as recorded by  
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    Diabetes Epidemiology and Management.2024; 13: 100191.     CrossRef
  • Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data
    Hye Ah Lee, Hyesook Park, Young Sun Hong
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
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    Rishita Konda, Anuraag Ramineni, Jayashree J, Niharika Singavajhala, Sai Akshaj Vanka
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  • The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use
    Ji-Won Chun, Hun-Sung Kim
    Journal of Korean Medical Science.2023;[Epub]     CrossRef
  • Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features
    Nikita Jain, Bhaumik Patel, Manjesh Hanawal, Anurag R. Lila, Saba Memon, Tushar Bandgar, Ashutosh Kumar
    Metabolomics.2023;[Epub]     CrossRef
  • Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid‐stimulating hormone level
    Hyunah Kim, Da Young Jung, Seung‐Hwan Lee, Jae‐Hyoung Cho, Hyeon Woo Yim, Hun‐Sung Kim
    Journal of Diabetes.2022; 14(9): 620.     CrossRef
  • Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
    Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
    Journal of Personalized Medicine.2022; 12(11): 1899.     CrossRef

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