IoB-Based Intelligent Healthcare System for Disease Diagnosis in Humans

Internet of Behavior (IoB) refers to the use of Internet of Things (IoT) devices to track data, monitor, and influence human behavior. The increasing use of IoB has also led to the development of systems for disease detection, which can leverage IoT data to enhance the accuracy and speed of disease detection. In this context, an IoB-based system for disease detection has been proposed in this paper that uses data from various IoT devices, such as wearable sensors, to monitor and analyze human behavior. The system collects data on numerous physiological parameters, such as heart rate, blood pressure, and body temperature, and uses this data to identify patterns that may be indicative of a particular disease or health condition. This approach can detect diseases at an early stage, before symptoms appear, which increases the chance of effective treatments. It can also provide real-time feedback to healthcare providers, enabling them to make informed decisions about patient care. The proposed DenseNet-K-Nearest Neighbor (KNN)-based IoB healthcare system optimizes healthcare processes, supports clinical decision-making, and can be used to improve patient care. The proposed model was compared with existing algorithms such as Naive Bayes (NB), decision trees (DT), logistic regression (LR), Convolution Neural Network (CNN), and KNN. The results demonstrate that the proposed system has a greater accuracy of 97.66% than the other four algorithms. It is widely assumed that the proposed method can lower the risk of chronic diseases by detecting them early and lowering the cost of diagnosis, therapy, and doctor consultation.