Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

• Published in: BIO web of conferences Vol. 8; p. 2002
• Main Authors: Li, Jian, Huang, Qin, Dong, Minghua, Qiu, Wei, Jiang, Lixia, Luo, Xiaoting, Huang, Zhengchun, Chen, Shuiqin, Wu, Qinfeng, Ou-Yang, Lu, Wu, Qin, Liu, Lihua, Li, Shumei
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2017
• Subjects: Blood pressure; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diastolic pressure; equations; females; males; noninsulin-dependent diabetes mellitus; Physical activity; Prediction models; Regression analysis; Regression models; Risk analysis; Risk factors; Sensitivity; Simulation; Sleep; Smoking; Vegetables
• ISSN: 2117-4458; 2273-1709; 2117-4458
• DOI: 10.1051/bioconf/20170802002

Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P)=BMI × 0.735+ vegetables × (−0.671) + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287) + sleep ×(−0.009) +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P)=BMI ×1.979+ vegetables× (−0.292) + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287) + sleep × (−0.010).The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.