Research On Key Technologies Of Risk Analysis Of Respiratory System Diseases Based On Data Mining

In the past few decades,The advancement of technology has promoted the rapid development of the global economy and steadily improved the quality of people’s lives.However,it has been accompanied by an increasing rate of hospital admissions.Respiratory diseases are prone to infection,recurrence,and long treatment cycles.They have become one of the main diseases affecting human health causing a heavy burden to the world.With the development of computer science and technology and the wide application of big data and artificial intelligence,the analysis of the risk of respiratory diseases through the method of data mining can not only help people to have a more comprehensive understanding of the disease,but also improve the quality of the medical service system.Around the risk of respiratory diseases,three aspects of research have been conducted as follows:(1)Based on large-scale multi-source data,the relationship between air pollution exposure and hospital admissions for respiratory-related diseases are researched in this dissertation.A generalized additive model is constructed,and experiments are conducted from other angles such as gender,age,and season.Then the common effects of multiple pollutants are then explored,and the proportion of hospital admissions due to air pollutants is estimated.This experiment analyzes the impact of air pollutants on the number of hospital admissions for respiratory diseases from multiple aspects.(2)Prediction of readmission risk for patients with respiratory diseases based on machine learning is studied.To solve the problem of unbalanced data set,a new processing method is proposed.This method uses a machine learning model as the base model,and its core idea is to find out the wrongly classified data in the minority samples of the training set during each round of training.These data are copied and added to the training set again for a new round of training.After each training,the machine learning model is combined into the final classifier.(3)Analysis of comorbidity patterns of respiratory diseases based on complex networks is explored.The relative risk between diseases is calculated by counting the number of patients suffering from various diseases,and the disease risk network diagram is constructed.The label propagation algorithm is used to cluster all diseases in the network graph and explore the connections between each disease.