Development of a Physical Impairment Prediction Model for Korean Elderly People using Synthetic Minority Over-Sampling Technique and XGBoost

Abstract

The old people's 'physical functioning' is a key factor of active ageing as well as a major factor in determining the quality of life and the need for long-term care in old age. Previous studies that identified factors related to ADL mostly used regression analysis to predict groups of high physical impairment risk. Regression analysis is useful for confirming individual risk factors, but has limitations in grasping multiple risk factors. As methods for resolving this limitation of regression models, machine learning ensemble boosting models such as random forest and eXtreme Gradient Boosting (XGBoost) are widely used. Nonetheless, the prediction performances of XGBoost, such as accuracy and sensitivity, remain to be verified additionally by follow-up studies. This article proposes an effective method of dealing with imbalanced data for the development of ensemble-based machine learning, by comparing the performances of disease data sampling methods. This study analyzed 3,351 old people aged 65 or above who resided in local communities and completed the survey. As machine learning models to predict physical impairment in old age, this study compared the logistic regression model, XGBoost and random forest, with respect to the predictive performances of accuracy, sensitivity, and specificity. This study selected as the final model a model whose sensitivity and specificity were 0.6 or above and whose accuracy was highest. As a result, synthetic minority over-sampling technique (SMOTE)-based XGBoost whose accuracy, sensitivity, and specificity were 0.67, 0.81, and 0.75, respectively, was determined as the most excellent predictive performance. The results of this study suggest that in case of developing a predictive model using imbalanced data like disease data, it is efficient to use the SMOTE-based XGBoost model.