Prediction of Self-rated Health of Older Adults by Network Services Based on Agent Simulation and XGBoost Algorithm

As the aging population grows, health issues have attracted widespread attention, especially among older adults. We explored the effects and simulation prediction of network services based on a multi-dimensional analysis of the health status of older adults, which could help older adults to better manage and evaluate their health. The study was conducted as follows. Based on the China Family Panel Studies (CFPS) data, a chi-square test was used to screen out 16 indicators with significant effects on the self-rated health (SRH) of older adults. To eliminate selection bias between samples, a propensity score matching (PSM) was used to explore the potential impact of network services on SRH of older adults. A multi-agent simulation model was constructed to examine SRH effects and then compare the health both before and after using network services based on the AnyLogic platform. The XGBoost algorithm was used to build a prediction model for assessing SRH of older adults. The experimental results show that network services have a positive effect on SRH of older adults using the multiple PSM methods, improving SRH of older adults by 14.1%. Meanwhile, the multi-agent simulation proves that network services can improve the health status of older adults. It also proves that the XGBoost algorithm has better accuracy, specificity, and running time than the other compared algorithms, and can meet the prediction needs of this paper. This study may enrich and expand the theoretical framework of health influence mechanism and simulation prediction studies.