基于深度神经网络的糖尿病发病风险评估OA

This study constructed a diabetes mellitus risk assessment model based on deep neural network.The experimental data were from the NAGALA(1994-2016)cohort,including 19 characteristics such as demographic information and biochemical indicators.An adaptive sampling method based on clustering is adopted to solve the problem of class imbalance.Then,a three-layer deep neural network model is constructed,and the SHAP analysis method is used to divide the high and low risk groups.Finally,the performance of the model is verified by comparative analysis and risk assessment effect analysis.The results showed that HbA1c,FPG,age and BMI were the main influencing factors of diabetes mellitus.The deep neural network model can effectively distinguish whether the patient is sick in each follow-up period,and its performance is better than other machine learning models.There are significant differences in the overall and specific indicators between the high and low risk groups.The diabetes mellitus risk assessment model can accurately predict the incidence and risk of diabetes mellitus,and provides a powerful tool for early screening and risk assessment of diabetes mellitus.