Abstract:Objective To explore the application of logistic regression and random forest to prediction of diabetes mellitus risk in health check-up population. Methods We selected 11,769 non-diabetic individuals who participated in a health check-up in Physical Examination Center, Beijing Aerospace General Hospital from January 2006 to December 2015, and then randomly selected 70% samples to establish two diabetes prediction models, which took 14 factors (including sex, age, body mass index, history of smoking, history of alcohol consumption, previous history of hypertension, family history of hypertension, family history of diabetes, systolic pressure, diastolic pressure, fasting blood-glucose, total cholesterol, triglyceride and fat liver) as arguments and whether or notdeveloping diabetes within 5 years as dependent variable, and were respectively developed by logistic regression and random forest. The remaining 30% samples were used as the validation set, and the predictive performance of different models was evaluated using the area under the receiver operating characteristic curve (AUC). Results The area under the receiver operating characteristic curve was 0.912 (95%CI:0.898-0.927) for logistic regression prediction model and 0.919 (95%CI:0.906-0.932) for random forest prediction model. With optimal cutoffs, logistic regression prediction model and random forest prediction model had a sensitivity of 80.8% and 84.1% respectively, and a specificity of 87.3% and 85.3% respectively. Conclusions Logistic regression prediction model and random forest prediction model both have good predictive performance in diabetes mellitus risk forecast among health check-up population.
白江梁, 张超彦, 李伟, 倪娜娜, 张莎, 胡升星, 刘宝花. 某医院体检人群糖尿病预测模型研究[J]. 实用预防医学, 2018, 25(1): 116-118.
BAI Jiang-liang, ZHANG Chao-yan, LI Wei, NI Na-na, ZHANG Sha, HU Sheng-xing, LIU Bao-hua. Diabetes prediction models based on health check-up population in a hospital. , 2018, 25(1): 116-118.
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