In recent years, fog computing emerges as a proactive solution for healthcare service as it facilitates continuous monitoring of remote patient health and early detection of mosquito-borne diseases. In addition, fog computing reduces the latency and communication cost that is normally an immense concern of cloud computing. The key objective of the proposed intelligent system is to detect and control the mosquito-borne diseases at the early stage. For this purpose, wearable and IoT sensors are used to gather the required information and fog computing is used to analyze, categorize and share medical information among the user and healthcare service providers. We utilize similarity coefficient to differentiate the various mosquito-borne diseases based on patient's symptoms, and the fuzzy k-nearest neighbor approach is employed to categorize the user into infected or uninfected class. Further, on the cloud layer, Social Network Analysis (SNA) is employed to represent the outbreak of mosquito-borne diseases. The likelihood of the registered user to receive or spread the disease is measured by computing PDO (Probability of Disease Outbreak) which is used to provide the location-based awareness to avert the outbreak. The experimental evaluation reveals the improved performance of the proposed F-HMRAS with 95.9% classification accuracy. © 2018 Elsevier Ltd