Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining Constrained Association Rules to Predict Heart Disease
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier
Expert Systems with Applications: An International Journal
Intelligent heart disease prediction system using data mining techniques
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Autonomous classifiers with understandable rule using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
Research on a Distributed Network Intrusion Detection System Based on Association Rule Mining
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Associating IDS Alerts by an Improved Apriori Algorithm
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
A Hybrid Classification Algorithm Evaluated on Medical Data
WETICE '10 Proceedings of the 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises
International Journal of Computational Intelligence Studies
Using classification to evaluate the output of confidence-based association rule mining
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Misleading Generalized Itemset discovery
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
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.