ACM Computing Surveys (CSUR)
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A framework of combining Markov model with association rules for predicting web page accesses
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Integrating recommendation models for improved web page prediction accuracy
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
Predicting individual disease risk based on medical history
Proceedings of the 17th ACM conference on Information and knowledge management
Time to CARE: a collaborative engine for practical disease prediction
Data Mining and Knowledge Discovery
An integrated model for next page access prediction
International Journal of Knowledge and Web Intelligence
A comorbidity network approach to predict disease risk
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
A comorbidity-based recommendation engine for disease prediction
CBMS '10 Proceedings of the 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems
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An approach for disease prediction that combines clustering, Markov models and association analysis techniques is proposed. Patient medical records are clustered and a Markov model for each cluster is generated to perform prediction of illnesses a patient could likely be affected in the future. However, when the probability of the most likely state in the Markov models is not sufficiently high, it resorts to sequential association analysis, by considering the items induced by high confidence rules generated by recurring sequential disease patterns. Experimental results show that the combination of different models enhances predictive accuracy and is a feasible way to diagnose diseases.