Assessing Health Symptoms on Intelligent IoT-based Healthcare System

The increased proportion of aging population will demand changes to healthcare systems in developing countries. The increasing demand on the health sector is an inevitable and complicated process. Providing suitable healthcare services is essential for those elderly people who may encounter various medical problems and conditions. Early detection and identification of diseases for treatment, and evaluation of the best alternatives can minimize the involved complications of daily activities for the aged people. To lower the risk for complications we develop an intuitive way of finding the uncertainty of health symptoms by symptom checker. The goal of this empirical study is to further examine and predict certain variations of diseases based on the symptoms that a patient reported. This recommended solution combines conceptual design and technique of multi criteria decision making (MCDM) analytic hierarchy process (AHP) with fuzzy triangular priority weights to deal with the uncertainty of imprecision and ambiguity that resulted from the relative priority scales of various factors of diseases. The method can help users to understand the uncertainty degree of a problem before making the final decision. This paper also validates the efficiency and feasibility of the proposed model to determine the weights of various diseases based on the symptoms and scores chosen by the users thereby providing quality of life assistance services.