Exploring Factors Associated with Subjective Health of Older-Old using ReLU Deep Neural Network and Machine Learning

Abstract

Resolving the health issues of the elderly has emerged as an important task in the current society. This study developed models that could predict the subjective health of the older-old based on gradient boosting machine (GBM), naive Bayes model, classification and regression trees (CART), deep neural network, and random forest by using the health survey data of the elderly and compared their prediction performance (i.e., accuracy, sensitivity, specificity) the models. This study analyzed 851 older-old people (≥75 years old) who resided in the community. This study compared the accuracy, sensitivity, and specificity of the developed models to evaluate their prediction performance. This study conducted 5-fold cross-validation to validate the developed models. The results of this study showed that the deep neural network with an accuracy of 0.75, a sensitivity of 0.73, and a specificity of 0.81 was the model with the best prediction performance. The normalized importance of variables derived from deep neural network analysis showed that depression, subjective stress recognition, the number of accompanying chronic diseases, subjective oral conditions, and the number of days walking more than 30 minutes were major predictors for the subjective health of the older-old. Further studies are needed to identify factors associated with the subjective health of the older-old with considering the age-period-cohort effects.