In the era of big data,machine learning technology has shown unique advantages in prediction accuracy and computa-tional efficiency.Combining traditional statistical analysis models with machine learning algorithms to construct more accurate pri-cing methods for long-term care insurance products in China has become a positive and beneficial exploration.This article divides the health status of elderly people into six categories based on international disability standards.Using data from the CHARLS da-tabase in 2018 and 2020,the insured population is expanded to be over 40 years old.The XGBoost Logistic combination model based on PSO algorithm is used to analyze the factors affecting health status.The bidirectional long short-term memory network(BiLSTM)is used to calculate the transition probability,and the CBD-LSTM combination model is used to calculate the expected life expectancy,in order to accurately price long-term care insurance in China.
关键词
长期护理保险/XGBoost-Logistic组合模型/BiLSTM模型/CBD-LSTM组合模型
Key words
long term care insurance/XGBoost-Logistic combination model/BiLSTM model/CBD-LSTM combination model
维普
万方数据