Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel-Alsina...

Area of Science:

• Healthcare Informatics
• Decision Science
• Artificial Intelligence in Medicine

Background:

• Tracking disability progression is challenging due to uncertain and imprecise health data.
• Existing multi-criteria decision-making (MCDM) methods are inadequate for complex, fuzzy medical data.

Purpose of the Study:

• To develop a novel classification framework for monitoring disability progression.
• To address limitations in handling uncertainty and imprecision in medical data.

Main Methods:

• A hybrid approach combining Tamir's complex fuzzy logic with the Aczel-Alsina weighted aggregated sum product assessment (WASPAS) method.
• Utilizing complex fuzzy logic for multidimensional uncertainty and Aczel-Alsina functions for flexible aggregation.
• Applying the framework to classify AI-powered predictive models for disability monitoring.

Main Results:

• The proposed framework significantly improves classification accuracy for AI models in disability tracking.
• Demonstrates enhanced decision support capabilities for healthcare planning.
• Case study validates the method's robustness, sensitivity, and effectiveness.

Conclusions:

• The novel complex fuzzy WASPAS framework offers a robust solution for disability progression tracking.
• Enhances the reliability and accuracy of AI-driven healthcare decision-making.
• Provides a valuable tool for managing and planning care for individuals with disabilities.