A machine learning-based online web calculator to aid in the diagnosis of sarcopenia in the US community.

Background: Sarcopenia places a heavy healthcare burden on individuals and society. Recognizing sarcopenia and intervening at an early stage is critical. However, there is no simple and easy-to-use prediction tool for diagnosing sarcopenia. The aim of this study was to construct a well-performing online web calculator based on a machine learning approach to predict the risk of low lean body mass (LBM) to assist in the diagnosis of sarcopenia. Methods: Data from the National Health and Nutritional Examination Surveys 1999-2004 were selected for model construction, and the included data were randomly divided into training and validation sets in the ratio of 75:25. Six machine learning methods- Classification and Regression Trees, Logistic Regression, Neural Network, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost)-were used to develop the model. They are screened for features and evaluated for performance. The best-performing models were further developed as an online web calculator for clinical applications. Results: There were 3046 participants enrolled in the study and 815 (26.8%) participants with LBM. Through feature screening, height, waist circumference, race, and age were used as machine learning features to construct the model. After performance evaluation and sensitivity analysis, the XGBoost-based model was determined to be the best model with better discriminative performance, clinical utility, and robustness. Conclusion: The XGBoost-based model in this study has excellent performance, and the online web calculator based on it can easily and quickly predict the risk of LBM to aid in the diagnosis of sarcopenia in adults over the age of 60.