Association rule mining to detect factors which contribute to heart disease in males and females

► UCI Heart disease Cleveland dataset used in this research. ► A computational intelligence approach is used to identify heart disease risk factors. ► Gender specific analysis is performed. ► This research shows females have less chance of coronary heart disease than males. ► Gender specific signifi...

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Bibliographic Details
Published in:Expert systems with applications 2013-03, Vol.40 (4), p.1086-1093
Main Authors: Nahar, Jesmin, Imam, Tasadduq, Tickle, Kevin S., Chen, Yi-Ping Phoebe
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Association rule mining
Cleveland data
Computational intelligence
Computer science
control theory
systems
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Expert systems
Female
Females
Healthy
Heart disease
Heart diseases
Intelligence
Male
Males
Memory organisation. Data processing
Men
Sick
Software
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Summary:► UCI Heart disease Cleveland dataset used in this research. ► A computational intelligence approach is used to identify heart disease risk factors. ► Gender specific analysis is performed. ► This research shows females have less chance of coronary heart disease than males. ► Gender specific significant factors are determined. This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms – Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.08.028