A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring

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.