Development of a Frailty Detection Model Using Machine Learning with the Korean Frailty and Aging Cohort Study Data
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Objectives: This paper aimed to use machine learning to identify a new group of factors predicting frailty in the elderlypopulation by utilizing the existing frailty criteria as a basis, as well as to validate the obtained results. Methods: This studywas conducted using data from the Korean Frailty and Aging Cohort Study (KFACS). The KFACS participants were classifiedas robust or frail based on Fried’s frailty phenotype and excluded if they did not properly answer the questions, resulting in1,066 robust and 165 frail participants. We then selected influential features through feature selection and trained the modelusing support vector machine, random forest, and gradient boosting algorithms with the prepared dataset. Due to the imbalanceddistribution in the dataset with a low sample size, holdout was applied with stratified 10-fold and cross-validationfor estimating the model performance. The reliability of the constructed model was validated using an unseen test set. Themodel was then trained with hyperparameter optimization. Results: During the feature selection process, 27 features wereidentified as meaningful factors for frailty. The model was trained based on the selected features, and the weighted averageF1-score reached 95.30% with the random forest algorithm. Conclusions: The results of the study demonstrated the possibilityof adopting machine learning to strengthen existing frailty criteria. As the method analyzes questionnaire responses in ashort time, it can support higher volumes of data on participants’ health conditions and alert them regarding potential risksin advance.
Abstract
Objectives: This paper aimed to use machine learning to identify a new group of factors predicting frailty in the elderlypopulation by utilizing the existing frailty criteria as a basis, as well as to validate the obtained results. Methods: This studywas conducted using data from the Korean Frailty and Aging Cohort Study (KFACS). The KFACS participants were classifiedas robust or frail based on Fried’s frailty phenotype and excluded if they did not properly answer the questions, resulting in1,066 robust and 165 frail participants. We then selected influential features through feature selection and trained the modelusing support vector machine, random forest, and gradient boosting algorithms with the prepared dataset. Due to the imbalanceddistribution in the dataset with a low sample size, holdout was applied with stratified 10-fold and cross-validationfor estimating the model performance. The reliability of the constructed model was validated using an unseen test set. Themodel was then trained with hyperparameter optimization. Results: During the feature selection process, 27 features wereidentified as meaningful factors for frailty. The model was trained based on the selected features, and the weighted averageF1-score reached 95.30% with the random forest algorithm. Conclusions: The results of the study demonstrated the possibilityof adopting machine learning to strengthen existing frailty criteria. As the method analyzes questionnaire responses in ashort time, it can support higher volumes of data on participants’ health conditions and alert them regarding potential risksin advance.
참고문헌 (25)
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