Cognitive Intelligence Assisted Fog-Cloud Architecture for Generalized Anxiety Disorder (GAD) Prediction

Generalized Anxiety Disorder (GAD) is a psychological disorder caused by high stress from daily life activities. It causes severe health issues, such as sore muscles, low concentration, fatigue, and sleep deprivation. The less availability of predictive solutions specifically for individuals suffering from GAD can become an imperative reason for health and psychological adversity. The proposed solution aims to monitor health, behavioral and environmental parameters of the individual to predict health adversity caused by GAD. Initially, Weighted-Naïve Bayes (W-NB) classifier is utilized to predict irregular health events by classifying the captured data at the fog layer. The proposed two-phased decision-making process helps to optimize the distribution of required medical services by determining the scale of vulnerability. Furthermore, the utility of the framework is increased by calculating health vulnerability index using Adaptive Neuro-Fuzzy Inference System-Genetic Algorithm (ANFIS-GA) on the cloud. The presented work addresses the concerns in terms of efficient monitoring of anomalies followed by time sensitive two-phased alert generation procedure. To approve the performance of irregular event identification and health severity prediction, the framework has been conveyed in a living room for 30 days in which almost 15 individuals by the age of 68 to 78 years have been continuously monitored. The calculated outcomes represent the monitoring efficiency of the proposed framework over the policies of manual monitoring.