To construct a forecasting model of the anthropometric chronic disease risk factor score

Background: Many health indices have a relationship with anthropometric indices. This research attempts to provide a new measurement: a chronic disease risk factor score built into the regression model. This new model will help people visualize their health status and get multiple information during the process of the healthy examination. Methods: Data from 8,034 subjects were collected from the data bank of the Health Examination Center in Chang Gung Memorial Hospital. Related anthropometric indices and biochemical factors were selected and used to construct a regression model. The anthropometric indices used were body mass index, waist hip ratio, waist hip area ratio, health index, waist leg ratio and trunk leg ratio. Biochemical data included blood pressure, glucose, triglyceride, cholesterol and uric acid, combined to form an anthropometric chronic disease risk factor score. Results: Subjects under 45 years of age had the highest chronic disease risk factor score, and were selected to construct a regression model. The R-square of this model is 0.355; its predictive error is near 12%. After verification with a testing group, the regression model could be used to predict health status. Conclusion: The purpose of this study was to develop a new anthropometric chronic disease risk factor score by combining anthropometric indices and biochemical data. A multiple regression model was used to illustrate health status via anthropometric chronic disease risk factor scores for the subjects participating in the health examination. The results show that the chronic disease risk factor score is useful for prescribing relevant medical treatment as well as for other research.