ArticleComputational and mathematical methods in medicine2022
Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors at 3 institutions in 1 country.
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Abstract
We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20-64 years) from Tokyo and surrounding areas were recruited. Participants underwent an oral glucose tolerance test (OGTT) and completed a questionnaire regarding lifestyle and physical characteristics. They were classified into four glycometabolic categories based on the OGTT results: category 1: best glucose metabolism, category 2: low insulin sensitivity, category 3: low insulin secretion, and category 4: combined characteristics of categories 2 and 3. A total of 977 individuals were included; the ratios of participants in categories 1, 2, 3, and 4 were 46%, 21%, 14%, and 19%, respectively. Machine learning models (decision tree, support vector machine, random forest, and XGBoost) were developed for identifying the glycometabolic category using questionnaire responses. Then, the top 10 most important variables in the random forest model were selected, and another random forest model was developed using these variables. Its areas under the receiver operating characteristic curve (AUCs) to classify category 1 and the others, category 2 and the others, category 3 and the others, and category 4 and the others were 0.68 (95% confidence intervals: 0.62-0.75), 0.66 (0.58-0.73), 0.61 (0.51-0.70), and 0.70 (0.62-0.77). For external validation of the model, the same dataset of 452 Japanese adults in Hokkaido was obtained. The AUCs to classify categories 1, 2, 3, and 4 and the others were 0.66 (0.61-0.71), 0.57 (0.51-0.62), 0.60 (0.50-0.69), and 0.64 (0.57-0.71). In conclusion, our model could identify the glucose metabolism status using only 10 factors of lifestyle and physical characteristics. This model may help the larger general population without diabetes to understand their glucose metabolism status and encourage lifestyle improvement to prevent diabetes.
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