International Journal of Obesity · Mar 20, 2026
A sequential deep learning model for predicting people with obesity in adults aged 18-64 using physical fitness variables
By Li X, Sung Y, Zhang Y et al.
BACKGROUND/OBJECTIVES: Health of people with obesity is a global concern. We developed an explainable sequential deep learning model using nationally representative physical fitness data to predict people with obesity and to identify the most influential predictors. SUBJECTS/METHODS: We analyzed data from 204,334 adults collected between 2010 and 2023. A sequential neural network model was trained and evaluated using stratified 5-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, as well as MAE, MSE, and R². Explainability was examined using SHAP and LIME analyses to rank feature importance and interpret decision patterns. RESULTS: Across five stratified folds, the model achieved an overall accuracy of 0.87-0.88 (p < 0.001 vs. random). Fold 4 showed optimal performance (TN = 1,462; FN = 184; FP = 249; TP = 1,554), yielding an accuracy of 0.873 (precision = 0.866, recall = 0.855, F1 = 0.876, ROC-AUC = 0.95) and stabilizing at 20 epochs. For this model, MAE was 0.122, MSE was 0.041, and R² was 0.833, with an average prediction error of 0.171 for the first 50 samples. SHAP identified 20-m shuttle run estimated VO₂max (importance = 0.339), gender (0.2481), and relative grip strength (0.135) as the top predictors. LIME (intercept = 0.511, predicted=0.668, R² = 0.995) indicated that low estimated VO₂max ( < 28.71 ml/kg/min) and low relative grip strength ( < 38.17%) substantially increased the probability of obesity classification, particularly among females. CONCLUSIONS: This explainable sequential deep learning model accurately predicts people with obesity using physical fitness variables and highlights the critical role of cardiorespiratory fitness in obesity risk assessment and management.