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Internet of Things (IoT) refers to the practice of designing and modeling objects connected to the Internet through computer networks. In the past few years, IoT-based health care programs have provided multidimensional features and services in real time. These programs provide hospitalization for millions of people to receive regular health updates for a healthier life. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. In this paper, a disease diagnosis system is designed based on the Internet of Things. In this system, first, the patient's courtesy signals are recorded by wearable sensors. These signals are then transmitted to a server in the network environment. This article also presents a new hybrid decision making approach for diagnosis. In this method, a feature set of patient signals is initially created. Then these features go unnoticed on the basis of a learning model. A diagnosis is then performed using a neural fuzzy model. In order to evaluate this system, a specific diagnosis of a specific disease, such as a diagnosis of a patient's normal and unnatural pulse, or the diagnosis of diabetic problems, will be simulated.

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