基于1D-ICNN的高维度数据下老年自评健康预测方法OA

The self-rated health of the elderly is an important factor to reflect the health status of the elderly,and it is of great significance to provide reference for improving the health level of the elderly.In order to understand the main factors affecting the self-rated health of the rural elderly in China and achieve accurate prediction,this study first explored the mechanism of different influencing factors on the self-rated health of the elderly based on the survey data of the elderly care demand in Yueyang County,Hunan Province in 2022.Then,based on the significant influencing factors,an improved one-dimensional convolutional neural network(1D-ICNN)based on cross entropy and variable learning rate is proposed to construct a self-rated health prediction model for the elderly in the case of high-dimensional data features,so as to solve the problems of inaccurate prediction and instability of 1D-CNN.This study shows that the self-rated health of the elderly is related to factors such as education level,political outlook,marital status,occupation and annual income.In the case of higher dimensional data features,the 1D-ICNN model has better prediction results.The application and popularization of this method can provide an empirical basis for accurately predicting the health status of the elderly and achieving"healthy aging".