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Paper details
Number 4 - December 2024
Volume 34 - 2024
Optimizing infectious disease diagnostics through AI-driven hybrid decision making structures based on image analysis
Muhammad Ahsan, Robertas Damaševičius, Sarmad Shahzad
Abstract
Infectious diseases significantly impact global mortality rates, with their complex symptoms complicating the assessment
and determination of infection severity. Various countries grapple with different forms of these diseases. This research
utilizes three AI-based decision-making techniques to refine diagnostic processes through the analysis of medical imagery.
The goal is achieved by developing a mathematical model that identifies potential infectious diseases from medical images,
adopting a multi-criteria decision-making approach. The avant-garde, AI-centric methodologies are introduced, harnessing
an innovative amalgamation of hypersoft sets in a fuzzy context. Decision-making might include recommendations for
isolation, quarantine in domestic or specialized environments, or hospital admission for treatment. Visual representations
are used to enhance comprehension and underscore the importance and efficacy of the proposed method. The foundational
theory and outcomes associated with this innovative approach indicate its potential for broad application in areas like
machine learning, deep learning, and pattern recognition.
Keywords
medical imaging, fuzzy logic, disease diagnostics, decision support, health informatics