Design of an Intelligent Pre-Alarm System for Chronic Diseases Among Urban Residents Integrating Machine Learning and Social Medicine Indicators

This study focused on the prevention and control of chronic diseases among urban residents and designed an intelligent pre-alarm system that integrated machine learning and social medical indicators. By comprehensively sorting out the relevant literature, the social medical index system of chronic diseases among urban residents was constructed, which covered social economy, lifestyle, and other dimensions. Based on a support vector machine algorithm, an intelligent pre-alarm algorithm model of chronic diseases was constructed through data preprocessing, feature selection, and other steps and verified by experiments. The experimental data came from several urban community health service centers. After dividing 4500 valid data into a training set and test set by a 7:3 ratio, the experiment was carried out. The results showed that the model performs well in accuracy, recall, F1 value and area under the curve. Under certain parameters, the accuracy rate was 85%, the recall was 80%, the F1 value was 0.82, and the area under the curve value was stable at about 0.88.