A communicable disease may be passed from one person to another by direct physical contact, inhalation of an airborne virus, or insect bites, among other routes of transmission. Wireless Body Area Network (WBAN) has become more important in the many IoT-based healthcare applications that have gained attraction in recent years. An effective response to these difficulties, Cognitive Computing (CC) is a branch of artificial intelligence that drives knowledge-rich automation. To identify infectious diseases in advance, finding the optimal fog node in the fog layer is an important task. Two cognitive engines, the data cognition engine and the resource cognition engine, analyze the information to find a solution. The current bodily positioned model, represented by the physical signal, is included in the data collected by the data cognition Engine from external cognition resources. Cognitive computing will be granted permission by the Resource cognition engine to use all available computing and communication resources outside of the network. In this article, we discuss the existence of both emergency and non-emergency fog nodes. If a patient is having a heart attack, for example, the system will assess the situation and determine whether or not to send them to the regular fog node, while sending patients with less urgent needs to the emergency fog node. The results of the comparison show that the suggested FogCog is superior to the established benchmark methods.