| This thesis is mainly the research and development of coronary heart disease (CHD) database system of data mining association rules. The association rule data mining technology was combined with the medical field. The oringinal records of the patients with coronary heart disease were analyzed using multi-dimensional association rule technology in data mining. The new recording sheet for coronary heart disease was formed by cleaning the initial large data row and extracting record field data associated with coronary heart disease according to coronary heart disease risk factors. The combination of frequent field composed of the morbidity records field value and the result field value of coronary heart disease was identified from the table. Then we measured whether association rule of coronary heart disease could be deduced by the combination of frequent field through the pre-given support and confidence.This paper designed and implemented the association rule data mining algorithm, analyzed the problems and deficiencies of the classical algorithm and improved the classical algorithm to increase the efficiency of algorithm. The bigger the database scale is, the more cycle index is needed when we used the new algorithm to process database. Every time the system processed the database, the record in newly generated database would be reduced a lot. Finally the least suitable data record was created. When the improved algorithm was used to process the database, the time of scanning and comparing the database records greatly decreased, thus enhancing the database mining efficiency.This thesis investigated the mental and behavioral factors'impact on health and disease, provided the data support that is necessary and further strengthened the study of coronary disease database. The developed database association rule mining system provides the good support for preventing coronary heart disease. In addition, this system is also suitable for other related domains. |
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