Theory and Implementation of Intelligent System for Auxiliary Diagnosis of Chronic Disease

With the accelerated development of internet and information technology, the healthcare industry has undergone tremendous changes. Big data, cloud computing and intelligent algorithms are playing an important role in the medical field. Due to unbalanced economic development, there is a large gap between medical care in urban and rural areas. However, most patients with chronic disease live in rural and remote areas. In order to enable low-income groups to have access to advanced medical resources, this paper designs an intelligent healthcare system for chronic disease auxiliary diagnosis. Taking heart disease, the most common disease in daily life, as the research object, an ensemble learning algorithm based on stacking is explored to predict the early diagnosis of heart disease for users. The results show that the ensemble learning algorithm is better than a single machine learning algorithm, and the accuracy of the prediction can be greatly improved. It is expected that this can be used to improve diagnosis in rural and remote areas.