Prof. Mukesh Kumar Sharma

Type-2 Fuzzy Logic with Metaheuristic Optimization Algorithms in Medical Diagnosis

Accurate disease monitoring is an extremely time-consuming task for medical experts and technocrats involved, requiring technical support for diagnostic systems. To overcome this situation, an approach of Internet of Medical Things (IoMT) based on Tsukamoto Type 2 Fuzzy Inference System (TT2FIS) be applied to handle diagnostic and predictive aspects in the medical field. The framework begins with an overview of Type-1 and Type-2 Fuzzy Logic to highlight their ability to handle uncertainty and imprecision in medical and diagnostic contexts.

Metaheuristic algorithms, such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and the Firefly Algorithm (FA), are applied for optimizing in enhancing the computational efficiency and accuracy. A case study on asthma diagnosis illustrates how PSO and Type-2 Fuzzy Logic can model symptoms, using eight input factors to categorize disease severity with adaptive fuzzy rules. This approach is further complemented by SA and FA, which bring unique optimization techniques inspired by natural processes, offering alternative solutions to minimize errors in predictive healthcare models. Overall, the integration of Type-2 Fuzzy Logic with metaheuristic algorithms presents a robust methodology for advancing accuracy in medical diagnosis to prove valuable in conditions requiring nuanced, adaptable decision systems.