Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China - Li, Ziyi; Yang, Na; He, Liyun; Wang, Jialu; Ping, Fan; Li, Wei; Xu, Lingling; Zhang, Huabing; Li, Yuxiu (Frontiers in Public Health, 2023)
Background: Considering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice. Methods: Two national cohort, namely, the China Health and Nutrition Survey (CHNS) and the China Health and Retirement Longitudinal Study were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated. Results: In the internal validation set, all the ML models performed better than the traditional Cox model and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80–0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77–0.87), 0.77 (95%CI: 0.75–0.79), and 0.79 (95%CI: 0.77–0.81), respectively, in predicting 2-, 9-, and 11-year mortality. Conclusions: In this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population.