A capsule network-based public health prediction system for chronic diseases: clinical and community implications.

Abstract

Objective: To observe the role of a public health chronic disease prediction method based on capsule network and information system in clinical treatment and public health management.

Methods: Patients with hypertension, diabetes, and asthma admitted from May 2022 to October 2023 were incorporated into the research. They were grouped into hypertension group (n = 341), diabetes group (n = 341), and asthma group (n = 341). The established chronic disease prediction method was used to diagnose these types of public health chronic diseases. The key influencing factors obtained by the prediction method were compared with the regression analysis results. In addition, its diagnostic accuracy and specificity were analyzed, and the clinical diagnostic value of this method was explored. This method was applied to public health management and the management approach was improved based on the distribution and prevalence of chronic diseases. The effectiveness and residents' acceptance of public health management before and after improvement were compared, and the application value of this method in public health management was explored.

Results: The key factors affecting the three diseases obtained by the application of prediction methods were found to be significantly correlated with disease occurrence after regression analysis (p < 0.05). Compared with before application, the diagnostic accuracy, specificity and sensitivity values of the method were 88.6, 89 and 92%, respectively, which were higher than the empirical diagnostic methods of doctors (p < 0.05). Compared with other existing AI-based chronic disease prediction methods, the AUC value of the proposed method was significantly higher than theirs (p < 0.05). This indicates that the diagnostic method proposed in this study has higher accuracy. After applying this method to public health management, the wellbeing of individuals with chronic conditions in the community was notably improved, and the incidence rate was notably reduced (p < 0.05). The acceptance level of residents toward the management work of public health management departments was also notably raised (p < 0.05).

Conclusion: The public health chronic disease prediction method based on information systems and capsule network has high clinical value in diagnosis and can help physicians accurately diagnose patients' conditions. In addition, this method has high application value in public health management. Management departments can adjust management strategies in a timely manner through predictive analysis results and propose targeted management measures based on the characteristics of residents in the management community.