A random forest classifier-based diabetes risk assessment model using community follow-up data achieved an accuracy of 91.24% and an AUC of 97%.
Observational (n=252,176)
Can a machine learning model based on community follow-up data accurately predict diabetes risk?
A machine learning model using community follow-up data can accurately predict diabetes risk, offering a potential tool for large-scale screening and personalized risk assessment.
Diabetes is a chronic metabolic disease characterized by hyperglycemia, the follow-up management of diabetes patients is mostly in the community, but the relationship between key lifestyle indicators in community follow-up and the risk of diabetes is unclear. In order to explore the association between key life characteristic indicators of community follow-up and the risk of diabetes, 252,176 follow-up records of people with diabetes patients from 2016 to 2023 were obtained from Haizhu District, Guangzhou. According to the follow-up data, the key life characteristic indicators that affect diabetes are determined, and the optimal feature subset is obtained through feature selection technology to accurately assess the risk of diabetes. A diabetes risk assessment model based on a random forest classifier was designed, which used optimal feature parameter selection and algorithm model comparison, with an accuracy of 91.24% and an AUC corresponding to the ROC curve of 97%. In order to improve the applicability of the model in clinical and real life, a diabetes risk score card was designed and tested using the original data, the accuracy was 95.15%, and the model reliability was high. The diabetes risk prediction model based on community follow-up big data mining can be used for large-scale risk screening and early warning by community doctors based on patient follow-up data, further promoting diabetes prevention and control strategies, and can also be used for wearable devices or intelligent biosensors for individual patient self examination, in order to improve lifestyle and reduce risk factor levels.
Jiang et al. (Sun,) conducted a observational in Diabetes (n=252,176). Random forest classifier-based diabetes risk assessment model was evaluated on Model accuracy and Area Under the Curve (AUC). A random forest classifier-based diabetes risk assessment model using community follow-up data achieved an accuracy of 91.24% and an AUC of 97%.