Abstract:
Background There are numerous frailty assessment scales, and most of the indicators are difficult to quantify objectively. The body composition analyzer can quickly obtain quantitative data related to frailty assessment, and machine learning has certain advantages in the mining and analysis of big data.
Objective To establish machine learning models based on body component data and evaluate its value in diagnosis and prediction of frailty.
Methods The physical examination data for the elderly over 65 years old in 10 Beijing communities were collected from April to June in 2021, and the Fried frailty phenotype scale was used as the gold standard for frailty diagnosis. Relevant indicators were screened , then random forest, support vector machine, logistic regression and XGBoost models were established to evaluate the predictive efficacy of the models using ROC curves, sensitivity and specificity.
Results A total of 558 cases were included for modeling analysis, including 122 pre-frailty cases and 436 non-frailty cases. The random forest algorithm screened important features such as age, 50kHz-whole body phase angle, skeletal muscle mass, and percent body fat, and four prediction models were built based on them. The logistic regression model had the highest overall predictive efficacy with an area under the ROC curve of 0.872, sensitivity and specificity of 78.38% and 80.15%, respectively, and a predictive accuracy of 79.76%. The overall effectiveness of the other three models did not differ significantly, with prediction accuracy exceeding 75%.
Conclusion The logistic regression model based on human body composition data is more effective than other machine learning models in predicting frailty in the elderly, with higher prediction accuracy, which can be used for early clinical diagnosis of frailty.
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