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.

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.

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