Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning - Zhang Q; Wan NJ (Diabetes, Metabolic Syndrome and Obesity, 2022)
Qian Zhang, Nai-jun Wan Department of Pediatrics, Beijing Jishuitan Hospital, Beijing, People’s Republic of ChinaCorrespondence: Nai-jun Wan, Department of Pediatrics, Beijing Jishuitan Hospital, 31# Xinjiekou Dongjie, West District, Beijing, 100035, People’s Republic of China, Tel +86-10-58398102, Email wann6971@163.comBackground: Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care.Patients and Methods: Seventeen features from 503 children 6– 12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods—namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)—were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected.Results: After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model.Conclusion: The model developed herein has a good predictive ability for IR in children 6– 12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.Keywords: children, insulin resistance, machine learning, artificial intelligence