Prediction of the Health Status of Older Adults Using Oversampling and Neural Network

Abstract

Self-rated health (SRH) serves as an important indicator for measuring the physical and mental well-being of older adults, holding significance for their health management and disease prevention. In this paper, we introduce a novel classification method based on oversampling and neural network with the objective of enhancing the accuracy of predict the SRH of older adults. Utilizing data from the 2020 China Family Panel Studies (CFPS), we included a total of 6596 participants aged 60 years and above in our analysis. To mitigate the impact of imbalanced data, an improved oversampling was proposed, known as weighted Tomek-links adaptive semi-unsupervised weighted oversampling (WTASUWO). It firstly removes the features that are not relevant to the classification by ReliefF. Consequently, it combines undersampling and oversampling. To improve the prediction accuracy of the classifier, an improved multi-layer perception (IMLP) for predicting the SRH was constructed based on bagging and adjusted learning rate. Referring to the experimental results, WTASUWO can effectively improve the prediction performance of a classifier when being applied on an imbalanced dataset, and the IMLP using WTASUWO achieves a higher accuracy. This method can more objectively and accurately assess the health status and identify factors affecting the SRH of older adults. By mining relevant information related the health status of older adults and constructing the prediction model, we can provide policymakers and healthcare professionals with targeted intervention techniques to focus on the health needs of older adults. Meanwhile, this method provides a practical research basis for improving the health level of older adults in China.