Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

Disease diagnosis is a challenging task due to large number of associated factors. Uncertainty in the process arises from inaccuracy patient attributes, missing data, and limitation medical expert's ability define cause effect relationships when there are multiple interrelated variables. This paper aims demonstrate an integrated view deploying smart disease using Internet Things (IoT) empowered by fuzzy inference system (FIS) diagnose various diseases. The Fuzzy System one best systems conditions because every involves many uncertainties, logic way handle uncertainties. Our proposed differentiates new cases provided symptoms disease. Generally, it becomes time-sensitive discriminate symptomatic can track firmly diseases through IoT FIS smartly efficiently. Different coefficients have been employed predict compute identified disease's severity for each sign study differentiate COVID-19, Typhoid, Malaria, Pneumonia. used method figure out over use given data related correlating with input symptoms. MATLAB tool utilised implementation FIS. procedure on aforementioned presents that affectionate derive results our proved could be other may assist doctors, patients, practitioners, healthcare professionals early better treat