Research On Association Model Of Air Quality And Chronic Disease

With the continuous development and progress of science and technology,the process of industrialization in our country has been accelerated and it has been rapidly developed.People's living standards have been significantly improved,and people are paying more and more attention to their own quality of life.As for the air we rely on to survive,we have been strenuously destroyed while the society has made great strides.As a result,air pollution has become more and more serious,and it has caused a great deal of harm to people's health.Its air quality affects people.Diseases and physical and mental health have brought a strong impact,and have now attracted people's attention and widespread concern in society.Every day,the air is constantly changing.It will not only reduce people's physical and mental satisfaction and satisfaction with life,but also reduce the body's resistance to various diseases,make people susceptible to the disease or even lead to increased disease or deterioration.Especially in the sudden change of weather,the concentration of various factors in the air has undergone drastic changes.The abrupt change in pressure and changes in environmental factors have made it extremely easy for people to erupt or even worsen the disease.According to statistics from related departments,the number of deaths from chronic diseases in China's population has accounted for 86.6% of the total deaths in the country's population.This data shows that among the causes of death,chronic diseases have occupied a very high proportion,and chronic diseases in our country have become harmful to people's mind and body.The first killer of health should be highly valued.The burden of economics,labor,and personnel caused by chronic diseases accounts for more than 70% of the total national disease burden.It has already been listed as an important issue that interferes with the country's rapid economic development and social harmony and stable development.Therefore,this article has developed a study of the association model between air quality and chronic diseases.By finding the correlation between air quality and chronic diseases,people can use their findings to prevent,slow down,suppress,or control their physical condition and plan their own science.life.The air quality data used in this paper comes from an ambient air quality monitoring station.In this data set,six air quality factors are selected;PM2.5,SO2,NO2,PM10,CO,and O3.The chronic disease data used were collected from chronic disease patients of the National Institute of Chronic Noncommunicable Disease Control in the Wuhan Municipal Center for Disease Control and Prevention.In order to be able to effectively and fully use the condition information data of patients with chronic diseases,it is also necessary to provide some To prevent and manage and control the scientific basis for the malignant development of chronic diseases,this association model selected the data of the death cause information of the chronic disease patients in the death cause monitoring module of the Institute of Chronic Noncommunicable Disease Control to conduct association rule mining.The research of this association model first consults domestic and foreign relevant literature,understands the current research status of data mining technology in air pollution and chronic diseases,and then elaborates on the relevant theories of data mining technology and association rules,which focuses on the Apriori algorithm in association rule mining.And FP-growth algorithm,in the study of the Apriori algorithm,understands and understands the running process of the algorithm,finds the disadvantages of its consumption in time,and analyzes some existing optimization methods for the algorithm.The algorithm provides reference for improvement.The improvement of the Apriori algorithm's operational efficiency is also improved.First,the SQL statements in the database are used to directly classify the data we use,and then the data is divided into blocks and mapped to each processor.In this way,when the data is scanned,as long as each processor scans its corresponding block,it is no longer necessary to scan as much as before,which greatly reduces the time spent scanning the data.Afterwards,a new data structure was used for experimentation.In the past,when dealing with databases,the database was viewed as a horizontal structure.Here,it is regarded as a vertical structure and is the data structure of the vertical correspondence relationship of project transactions.The optimization algorithm only needs to scan the database once,which avoids handling unnecessary transactions and items in the subsequent process.In the running process of the algorithm,the set of transaction items to be processed is also gradually reduced.In addition,the algorithm's optimization of the data structure is also relatively simple.By using collections,the operation of the collection is also relatively time-saving,thus avoiding the need to spend more time constructing complex data structures like other improved algorithms and avoiding certain things like These algorithms generate a lot of time consumption when constructing a matrix and multiplying the matrix.Moreover,the optimization algorithm only needs to process each transaction item at the beginning of the database scan.In the subsequent process,we only need to match the transaction code and do not need to match all items of the transaction.In addition,a feature of the transaction is the time series.The algorithm is based on the size of the transaction code,so that when computing the intersection of two sets,only the two sets need to be scanned sequentially instead of the cyclic scanning set to obtain the intersection of the two sets.The algorithm avoids a large number of pattern matching calculation transactions and does not require a cyclic scan set,thereby greatly improving the time efficiency of the algorithm.We conducted comparative analysis using data experiments to prove that the improved algorithm can speed up a lot in time,and because it can reduce the number of scans on the transaction database,it is much shorter in time,improving the operating efficiency.Then,the overall scheme of association rule mining for air quality and chronic diseases was designed.The collected data was first preprocessed,then minimum support and confidence were set.Then the improved Apriori algorithm was applied to the association rules mining of air quality and chronic diseases.Finally,we analyze the association rules generated by data mining and explore the relationship between air quality and chronic diseases.Through the conclusions of the study,we will understand the effects of air factors on chronic diseases and provide citizens with a more scientific understanding of air quality and human health,and achieve the effect of preventing disease and reducing disease levels.This has certain significance for the prevention and treatment of chronic diseases.We have a deeper understanding of the environment in which we need to protect our survival.