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Background/Objectives <p>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.</p> Subjects/Methods <p>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.</p> Results <p>Across five stratified folds, the model achieved an overall accuracy of 0.87–0.88 (<i>p</i> &lt; 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 ( &lt; 28.71 ml/kg/min) and low relative grip strength ( &lt; 38.17%) substantially increased the probability of obesity classification, particularly among females.</p> Conclusions <p>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.</p>

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A sequential deep learning model for predicting people with obesity in adults aged 18–64 using physical fitness variables

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Xinxing Li,
  • Yunho Sung,
  • Yanjie Zhang,
  • Byunggul Lim,
  • Dae Young Kim,
  • Jun-Hyun Bae

摘要

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.