MDS: Multi-level decision system for patient behavior analysis based on wearable device information

Smart healthcare devices and applications are designed to rely on intelligent sensing devices and wireless communication networks. The purpose of this integration is to provide better patient monitoring and facilitate modest disease diagnosis. Wireless biomedical sensing devices are placed in the patient's body for periodic and regular monitoring and updating of the sensed information. Based on this information, the behavior of the patient and nature of the disease were identified for use in further diagnosis and prediction. This manuscript introduces a multi-level decision system (MDS) for monitoring and detecting patient behavior based on sensed information. The information from the devices is matched with historical data to understand the current state of the patient's health. The process of decision making on the basis of the received sensor information at the medical center, is invoked for granting medical recommendations to the patients. MDS stores new phenomena in each patient's health for future evaluation and it determines the frequency for analysis based on the behavior of the patient. MDS is intended to reduce doctors' analysis and recommendation time, even with minimal information. MDS also conducts flexible information analysis in order to match patient behavioral analysis, so as to produce better recommendations. Experimental analysis of MDS proves its reliability by improving accuracy, true positive rate, F-measure score, and by reducing fusion delay.