Identifying factors associated with central obesity in school students using artificial intelligence techniques

OBJECTIVES: We, in a large survey of school students from Beijing, aimed to identify the minimal number of promising factors associated with central obesity and the optimal machine-learning algorithm. METHODS: Using a cluster sampling strategy, this cross-sectional survey was conducted in Beijing in early 2022 among students 6–14 years of age. Information was gleaned via online questionnaires and analyzed by the PyCharm and Python. RESULTS: Data from 11,308 children were abstracted for analysis, and 3,970 of children had central obesity. Light gradient boosting machine (LGBM) outperformed the other 10 models. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic of LGBM were 0.769982, 0.688312, 0.612323, 0.648098, and 0.825352, respectively. After a comprehensive evaluation, the minimal set involving top 6 important variables that can predict central obesity with descent performance was ascertained, including father's body mass index (BMI), mother's BMI, picky for foods, outdoor activity, screen, and sex. Validation using the deep-learning model indicated that prediction performance between variables in the minimal set and in the whole set was comparable. CONCLUSIONS: We have identified and validated a minimal set of six important factors that can decently predict the risk of central obesity when using the optimal LGBM model relative to the whole set.