Fog Computing Integrated with and Blockchain Technology for Accurate Disease Prediction

A modern computational paradigm known as fog computing provides distributed end users with very scalable and low-latency services. Because local fog nodes enable the quick storage and processing of data close to data sources, it has an inherent safety benefit over cloud computing. Simultaneously, the Blockchain (BC) technology represents a significant advancement, prioritizing data security, anonymity, and integrity via a consensus-building process. While BC was first widely used in finance, it is now finding its way into other industries, like as healthcare. In order to forecast diseases, this research suggests a safe healthcare service that uses Blockchain and fog computing. It focuses on diabetes and cardiovascular conditions. Health data about patients is collected from the fog nodes and safely kept on a blockchain. After grouping patient health data using a unique rule-based clustering approach, a feature selection-based neuro-fuzzy inference system with adaptive capability (FS-ANFIS) is used to forecast diseases. Significant real-world healthcare data trials are used to test the suggested technique. Performance measures, such as purity as normalized mutual information (NMI), are used to measure the effectiveness of rule-based clustering, as prediction accuracy is used to evaluate the performance of illness prediction. Comparing the testing results to other neural network methods, the suggested methodology achieved over 81% prediction accuracy, demonstrating its effectiveness.