Recommender system for the detection and prevention of dengue

In recent years huge amounts of information has been collected in health based databases depicting patients’ health status (e.g., as laboratory results, treatment plans, medical reports). Thus, diagnosis of patient’s health has been considered as one of the vital processes in health care expert systems for the detection of acquired diseases from the given symptoms of the affected person. Dengue fever is a mosquito-borne disease caused by the dengue virus that in recent years has become a major public health concern because it can result in the death of the affected person if no action is taken. This disease is caused by the mosquito bite infected by one of the four dengue virus surrogates. It is one of those diseases whose symptoms are hard to detect. The main problem with the dengue fever detection is many people cannot depict whether they are infected by dengue fever or not which make them do nothing as they thought it is only a normal fever. However, early clinical diagnosis and careful clinical management by trained can increase survival of patients. In that case some reliable system is needed that can predict the disease and allow the user to take the necessary steps. This paper discusses the development of a wellness recommender system that would help users to detect dengue. Fuzzy systems are widely used in health care systems and are one of the most common subjects of today’s Medical Informatics. This paper has proposed a model that makes use of expert system based on fuzzy logic that analyzes symptoms introduced by the user and formulates a diagnosis using fuzzy sets to detect whether person is infected with dengue or not. Thus the proposed model uses rules of fuzzy logic in order to check whether person’s symptoms are to be qualified to be in infected category or not.