A STUDY ON ASSOCIATION RULE MINING FOR VARIOUS HEART DISEASES MEDICAL DATA

information technology

This paper describes our experience on discovering association rules in medical data to predict heart disease. Heart disease is the leading causes of mortality accounting for 32% of all death, a rate is high as in Canada (35%) and USA. Association rule mining a computational intelligence approach is used to identify the factors that contribute to heart disease and Uci Cleveland data set, a biological data base is considered along with the rule generation algorithm – Apriori. Analyzing the information available on sick and healthy individuals and taking confidence as indicator. Females are seen to have more chance of being free from coronary heart disease than males. It is also seen that factors such as chest pain being asymptomatic and the presence of exercise- induced angina indicate the likely of existence of heart disease for both men and women. On the other hand, the result showed that when exercise induced angina (chest pain) was false, it was a good indicator of a person being healthy irrespective of gender. This research has demonstrated the use of rule mining to determine interesting knowledge.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.