Rapid advancement in Wireless Sensor Network (WSN) technology facilitates remote health care solutions without hindering the mobility of a person using Wearable Wireless Body Area Network (WWBAN). Activity recognition, fall detection and finding abnormalities in vital parameters play a major role in pervasive health care for making accurate decision on health status of a person. This chapter presents the proposed two pattern mining algorithms based on associative classification and fuzzy associative classification which models the association between the attributes that characterize the activity or health condition and handles the uncertainty in data respectively for an accurate decision making. The algorithms mine the data from WWBAN to detect abnormal health status of the person and thus facilitate remote health care. The experimental results on the proposed algorithms show that they work par with the popular traditional algorithms and predicts the activity class, fall or health status in less time compared to existing traditional classifiers.