Journal of global health · 2019
Development and validation of prediction models for hypertension risks in rural Chinese populations.
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Xu Fei, Zhu Jicun, Sun Nan, Wang Lu, Xie Chen, Tang Qixin, Mao Xiangjie, Fu Xianzhi, Brickell Anna, Hao Yibin, Sun Changqing
Аннотация
BACKGROUND: Various hypertension predictive models have been developed worldwide; however, there is no existing predictive model for hypertension among Chinese rural populations.
METHODS: This is a 6-year population-based prospective cohort in rural areas of China. Data was collected in 2007-2008 (baseline survey) and 2013-2014 (follow-up survey) from 8319 participants ranging in age from 35 to 74 years old. Specified gender hypertension predictive models were established based on multivariate Cox regression, Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), and Classification and Regression Tree (CART) in the training set. External validation was conducted in the testing set. The estimated models were assessed by discrimination and calibration, respectively.
RESULTS: During the follow-up period, 432 men and 604 women developed hypertension in the training set. Assessment for established models in men suggested men office-based model (M1) was better than others. C-index of M1 model in the testing set was 0.771 (95% confidence Interval (CI) = 0.750, 0.791), and calibration χ2 = 6.3057 (P = 0.7090). In women, women office-based model (W1) and ANN were better than the other models assessed. The C-indexes for the W1 model and the ANN model in the testing set were 0.765 (95% CI = 0.746, 0.783) and 0.756 (95% CI = 0.737, 0.775) and the calibrations χ2 were 6.7832 (P = 0.1478) and 4.7447 (P = 0.3145), respectively.
CONCLUSIONS: Not all machine-learning models performed better than the traditional Cox regression models. The W1 and ANN models for women and M1 model for men have better predictive performance which could potentially be recommended for predicting hypertension risk among rural populations.
METHODS: This is a 6-year population-based prospective cohort in rural areas of China. Data was collected in 2007-2008 (baseline survey) and 2013-2014 (follow-up survey) from 8319 participants ranging in age from 35 to 74 years old. Specified gender hypertension predictive models were established based on multivariate Cox regression, Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), and Classification and Regression Tree (CART) in the training set. External validation was conducted in the testing set. The estimated models were assessed by discrimination and calibration, respectively.
RESULTS: During the follow-up period, 432 men and 604 women developed hypertension in the training set. Assessment for established models in men suggested men office-based model (M1) was better than others. C-index of M1 model in the testing set was 0.771 (95% confidence Interval (CI) = 0.750, 0.791), and calibration χ2 = 6.3057 (P = 0.7090). In women, women office-based model (W1) and ANN were better than the other models assessed. The C-indexes for the W1 model and the ANN model in the testing set were 0.765 (95% CI = 0.746, 0.783) and 0.756 (95% CI = 0.737, 0.775) and the calibrations χ2 were 6.7832 (P = 0.1478) and 4.7447 (P = 0.3145), respectively.
CONCLUSIONS: Not all machine-learning models performed better than the traditional Cox regression models. The W1 and ANN models for women and M1 model for men have better predictive performance which could potentially be recommended for predicting hypertension risk among rural populations.