机器学习算法在体检人群糖尿病风险预测中的应用

目的 探索Logistic回归分析模型和LightGBM(light gradient boosting machine)算法对体检人群未来罹患糖尿病的预测效果及影响因素.方法 选取2003年8月—2019年4月在南方医院健康管理中心多次进行团体参检的36 292例非糖尿病人员,分层随机选取70％样本,以首次体检的性别...

Application of machine learning algorithm in diabetes risk prediction of physical examination population

  Objective  To explore the predictive effect and influencing factors of Logistic regression analysis model and Light GBM algorithm on the development of diabetes in the physical examination population.  Methods  A total of 36 292 subjects without diabetes were selected from the Health Management Center of Nanfang Hospital from August 2003 to April 2019. We ramdomly selected 70% samples by stratification to construct trainingset. The independent variables were 34 indicators including gender, age, body mass index (BMI), waist circumference, heart rate, systolic blood pressure, diastolic blood pressure, and fasting blood glucose in the first physical examination. We defined the dependent variable as developing diabetes within 5 years from the first physical examination.Logistic regression analysis model and LightGBM (light gradient boosting machine) algorithm was uesd to establish diabetes prediction models, respectively. The prediction model was applied to the remaining 30% samples and the area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the prediction effect.  Results  The AUC of the Logistic regression algorithm model was 0.906, while the AUC of the LightGBM analysis model was 0.910. At the optimal critical point, the sensitivity and specificity of the Logistic regression analysis model were 81.5% and 84.3%, respectively. And the sensitivity and specificity of the LightGBM analysis model were 81.6% and 85.2%, respectively.  Conclusion  The Logistic regression algorithm model and LightGBM algorithm model have good prediction effect on the development of diabetes in the physical examination population.