A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Scoring the Data Using Association Rules
Applied Intelligence
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
ICDE '95 Proceedings of the Eleventh 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
An introduction to variable and feature selection
The Journal of Machine Learning Research
A ubiquitous warning system for asthma-inducement
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Vol 2 - Workshops - Volume 02
Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
An Integrated Data Mining System for Patient Monitoring with Applications on Asthma Care
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Development of a Vital Sign Data Mining System for Chronic Patient Monitoring
CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
Designing patient-oriented systems with semantic web technologies
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
An open architecture patient monitoring system using standard technologies
IEEE Transactions on Information Technology in Biomedicine
Real time processing of data from patient biodevices
HIKM '11 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 120
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Chronic asthmatic sufferers need to be constantly observed to prevent sudden attacks. In order to improve the efficiency and effectiveness of patient monitoring, we proposed in this paper a novel data mining mechanism for predicting attacks of chronic diseases by considering of both bio-signals of patients and environmental factors. We proposed two data mining methods, namely Pattern Based Decision Tree (PBDT) and Pattern Based Class-Association Rule (PBCAR). Both methods integrate the concepts of sequential pattern mining to extract features of asthma attacks, and then build classifiers with the concepts of decision tree mining and rule-based method respectively. Besides the general clinical data of patients, we considered environmental factors, which are related to many chronic diseases. For experimental evaluations, we adopted the children asthma allergic dataset collated from a hospital in Taiwan as well as the environmental factors like weather and air pollutant data. The experimental results show that PBCAR delivers 86.89% of accuracy and 84.12% of recall, and PBDT shows 87.52% accuracy and 85.59 of recall. These results also indicate that our methods can perform high accuracy and recall on predictions of chronic disease attacks. The readable rules of both classifiers can provide patients and healthcare workers with insights on essential illness related information. At the same time, additional environmental factors of input data are also proven to be valuable in predicting attacks.