Big Data, Machine Learning, and Health Knowledge Discovery in the Elderly in China

According to the national strategic plan for healthy aging and the construction of the pension system in China, it is expected that by 2020 the population of elderly aged 60 and above will reach 255 million, accounting for about 17.8% of the total population. Currently, population aging is a serious social problem in China, and thus, health status of the elderly becomes increasingly critical. The present research uses machine learning to identify factors influencing elderly’s health status and life satisfaction with data from the Chinese Longitudinal Healthy Longevity Survey. The results show that some common factors are important for both self-rated health status and life satisfaction for elderly, namely positive and optimistic attitudes, a healthy diet, and economic status. Health status and life satisfaction also have their unique predicting factors, such as mobility ability for health status and living conditions for life satisfaction. Theoretical and practical implications of the findings are discussed.