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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Veröffentlicht in:	Expert systems with applications 2013-03, Vol.40 (4), p.1086-1093
Hauptverfasser:	Nahar, Jesmin, Imam, Tasadduq, Tickle, Kevin S., Chen, Yi-Ping Phoebe
Format:	Artikel
Sprache:	eng
Schlagworte:	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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