Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program
Abstract Objectives Since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in strategies to improve implementation of the program based on predictive analytics has grown. We investigated the performance of prediction models developed to identify individuals classified as “requiring instruction” (high-risk) who were unlikely to participate in a health intervention program. Methods Data were obtained from one large health insurance union in Japan. The study population included individuals who underwent at least one general health check-up between 2008 and 2013 and were identified as “requiring instruction” in 2013. We developed three prediction models based on the gradient boosted trees (GBT), random forest (RF), and logistic regression (LR) algorithms using machine-learning techniques and compared the areas under the curve (AUC) of the developed models with those of two conventional methods The aim of the models was to identify at-risk individuals who were unlikely to participate in the instruction program in 2013 after being classified as requiring instruction at their general health check-up that year. Results At first we performed the analysis using data without multiple imputation. The AUC values for the GBT, RF, and LR prediction models and conventional methods: 1, and 2 were 0.893 (95%CI: 0.882–0.905), 0.889 (95%CI: 0.877–0.901), 0.885 (95%CI: 0.872–0.897), 0.784 (95%CI: 0.767–0.800), and 0.757 (95%CI: 0.741–0.773), respectively. Subsequently, we performed the analysis using data after multiple imputation. The AUC values for the GBT, RF, and LR prediction models and conventional methods: 1, and 2 were 0.894 (95%CI: 0.882–0.906), 0.889 (95%CI: 0.887–0.901), 0.885 (95%CI: 0.872–0.898), 0.784 (95%CI: 0.767–0.800), and 0.757 (95%CI: 0.741–0.773), respectively. In both analyses, the GBT model showed the highest AUC among that of other models, and statistically significant difference were found in comparison with the LR model, conventional method 1, and conventional method 2. Conclusion The prediction models using machine-learning techniques outperformed existing conventional methods: for predicting participation in the instruction program among participants identified as “requiring instruction” (high-risk). Highlights The prediction models outperformed conventional methods for predicting participation in instruction program after general health check-up. There was no difference in performance between prediction models without multiple imputation and those with multiple imputation. The most important predictor were past participation in instruction program and diagnosis of general health check-up. This predictive method would contribute to select more appropriate intervention targets.
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