Medical Diagnosis Data Mining Based on Improved Apriori Algorithm

With the wide application of computer science and technology, the amount of data generated by various disciplines increased rapidly. In order to discover valuable knowledge in these databases, people use data mining methods to solve this problem. The application of association rule mining is an important research topic in data mining. As the association rule technology becomes more mature, it is a new research that how to use this method to find out the intrinsic association rules from a large number of medical data, providing an effective basis for clinical disease surveillance, evaluation of drug treatment and disease prevention. This paper uses Apriori, the classic algorithm of association rule, for data mining analysis of medical data. According to the characteristics of medical data, it improved the Apriori algorithm. Using the improved Apriori algorithm, it finds frequent item sets in a database of medical diagnosis, and generates strong association rules, in order to find out the useful association relationship or pattern between the large data item sets. The results show that, the improved Apriori algorithm can dig out association rule models about the properties and nature of the disease from a medical database, which can assist doctors in medical diagnosis. Therefore, it is a worthy research direction that using data mining method to process and analyze the data of disease prevention and drug treatment in the field of medicine.