Harnessing Biomedical Signals: A Modern Fusion of Hadoop Infrastructure, AI, and Fuzzy Logic in Healthcare

Health monitoring and assessment are becoming more difficult tasks for medical professionals and hospitals due to the rising number of patients and the advent of new diseases and symptoms. Indeed, many health-based sensor technologies face significant hurdles due to the analysis of large and heterogeneous data obtained by biological signals, as well as the requirement for patient classification and disease detection. As a result, an approach is developed in this paper to address the problems with physical infrastructure utilizing Hadoop-based systems. The Fuzzy Interface System Algorithm (FISA) is combined with a four-layer model, and data transfers at these layers are accomplished by using comparable health data that has been gathered at different treatment facilities. In this type of technique, an activation function is added in the center, whose performance attempts to minimize the loss of functionalities found in biological signals. A biological signal processing tool is used to simulate the effectiveness of the proposed paradigm. According to the data, FISA operates more effectively in terms of both cost and signal strength.