ABSTRACT

This study employed an integrated algorithm combining neural networks and random forests to develop a chronic disease diagnosis system for Dongguan Hospital in Guangdong Province, China. The system facilitated the early diagnosis of chronic diseases and provided decision support, thereby enhancing the efficiency of chronic disease diagnosis and exhibiting extensive potential for application. The study proposed a novel integrated algorithmic model by leveraging the advantages of neural networks and random forest algorithms. This integrated algorithm analyzed patients’ statistical data, predicted the early diagnosis of chronic diseases, and generated personalized diagnostic results and treatment recommendations. By tailoring medical plans to the specific conditions of individual patients, the system improved treatment outcomes and patient satisfaction. Furthermore, through the collection, processing, analysis, and prediction of data, the system aimed to enhance the efficiency and quality of healthcare services for chronic diseases.

Keywords: chronic diseases diagnosis system, decision support, integrated algorithmic model, neural network algorithm, random forest algorithm
https://doi.org/ 10.57180/mvas5504