Sequential Deep Learning Model for Obesity Prediction Based on Physical Fitness Factors: An Analysis of Data from the 2010–2023 Korean National Physical Fitness Data

Abstract

Background Obesity, a "global syndemic," increases the risk of noncommunicable diseases; therefore, the prediction and management of obesity is crucial. Regular physical activity and cardiorespiratory fitness are inversely correlated with obesity, highlighting the need for effective models for predicting obesity. Aim This study aimed to predict obesity using physical fitness factors, including those related to cardiorespiratory fitness, determined via deep neural network analysis of data obtained from the 2010–2023 Korean National Physical Fitness Award. Methods A deep learning approach was implemented to analyze the data obtained from 108,304 participants, and variables such as exercise-induced oxygen consumption during a 20-m shuttle run test (20-m VO₂ max), gender, and relative grip strength were considered. Stratified K-fold cross-validation, SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic description analyses were employed to evaluate the model performance and feature importance. Results The neural network yielded a high accuracy score (0.87–0.88), with Fold 4 providing the optimized model for obesity classifications. Features such as 20-m VO₂ max, gender, and relative grip strength significantly influenced the obesity predictions, and low 20-m VO₂ max levels were key predictors of obesity. Discussion This study confirmed the efficacy of the proposed deep neural network in predicting obesity based on physical fitness factors and clarified the significant predictors of obesity. Conclusion The results of this study may potentially be used for devising personalized obesity-management strategies that emphasize the importance of cardiorespiratory fitness.