Prediction of Type 2 Diabetes Based on Machine Learning Algorithm
Department of Information and Communications Engineering, Myongji University, 116 Myongji-ro, Yongin, Gyeonggi 17058, Korea
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Academic Editor: Giuseppe Banfi
Int. J. Environ. Res. Public Health 2021, 18(6), 3317; https://doi.org/10.3390/ijerph18063317
Received: 2 February 2021
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Revised: 15 March 2021
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Accepted: 17 March 2021
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Published: 23 March 2021
Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.
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Keywords:
type 2 diabetes; machine learning; prediction
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MDPI and ACS Style
Deberneh, H.M.; Kim, I. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. Int. J. Environ. Res. Public Health 2021, 18, 3317. https://doi.org/10.3390/ijerph18063317
AMA Style
Deberneh HM, Kim I. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. International Journal of Environmental Research and Public Health. 2021; 18(6):3317. https://doi.org/10.3390/ijerph18063317
Chicago/Turabian StyleDeberneh, Henock M., and Intaek Kim. 2021. "Prediction of Type 2 Diabetes Based on Machine Learning Algorithm" International Journal of Environmental Research and Public Health 18, no. 6: 3317. https://doi.org/10.3390/ijerph18063317
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