# Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method

## TL;DR

This paper introduces a new method for classifying AI models used to track disability progression, using advanced fuzzy logic to handle uncertainty in health data.

## Contribution

The novel contribution is a hybrid classification framework using Tamir’s complex fuzzy Aczel-Alsina WASPAS method for improved accuracy in disability progression prediction.

## Key findings

- The proposed framework improves classification accuracy in disability progression tracking.
- The method demonstrates robustness and effectiveness in real-world healthcare scenarios.
- It enhances decision support for healthcare planning through better model evaluation.

## Abstract

Tracking the development of disability conditions presents significant challenges due to uncertainty, imprecision, and dynamic health progression patterns. Traditional multi-criteria decision-making (MCDM) techniques often struggle with such complex and fuzzy medical data. To address this gap, we propose a novel classification framework based on Tamir’s complex fuzzy Aczel-Alsina weighted aggregated sum product assessment (WASPAS) approach. This hybrid model incorporates complex fuzzy logic to handle multidimensional uncertainty and utilizes the Aczel-Alsina function for flexible aggregation. We apply this method to evaluate and classify AI-powered predictive models used for monitoring disability progression. The proposed framework not only improves classification accuracy but also enhances decision support in healthcare planning. A case study validates the robustness, sensitivity, and effectiveness of the proposed method in real-world disability tracking scenarios.