In this paper, we proposed an integrated data mining system for patient monitoring with applications on asthma care. In this system, two data mining methods named PBD and PBC are designed for predicting asthma attacks. The main methodology is to extract the significant information of asthma attacks and build classifiers by using users' daily bio-signal records and environmental data. Meanwhile, helpful medical information and suggestions supported by doctors are applied. In this way, the proposed system can predict the chances of asthma attacks and provide patients with the proper medical instructions or health messages. The experimental evaluation results proved that the proposed mechanism is effective and reliable in asthma attack prediction.
['Vincent S. Tseng', 'Chao-Hui Lee', 'J. Chia-Yu Chen']
An Integrated Data Mining System for Patient Monitoring with Applications on Asthma Care