COVID‐19 patient diagnosis and treatment data mining algorithm based on association rules

• Published in: Expert systems Vol. 40; no. 4; pp. e12814 - n/a
• Main Authors: Shan, Zicheng, Miao, Wei
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.05.2023; John Wiley and Sons Inc
• Subjects: Algorithms; association rules; Bioinformatics; COVID-19; COVID‐19 patients; Data analysis; Data mining; Data processing; data warehouse; Data warehouses; Decision analysis; Decision trees; diagnosis treatment data mining; Drug use; Health services; Hypertension; Knowledge bases (artificial intelligence); Medical diagnosis; online analytical processing; Original; association rules; data warehouse; COVID‐19 patients; diagnosis treatment data mining; online analytical processing
• ISSN: 0266-4720; 1468-0394; 1468-0394
• DOI: 10.1111/exsy.12814

Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID‐19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID‐19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre‐process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID‐19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision‐maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID‐19, CHD and hypertension, while 46.5% of people with COVID‐19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID‐19 disease.