An intelligent multi-attribute decision-making system for clinical assessment of spinal cord disorder using fuzzy hypersoft rough approximations DOI Creative Commons

Muhammad Abdullah,

Khuram Ali Khan, Atiqe Ur Rahman

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 10, 2025

The data for diagnosing spinal cord disorder (SCD) are complex and often confusing, making it difficult established diagnostic techniques to yield reliable results. This issue frequently necessitates expensive testing get an accurate diagnosis. However, the process can be enhanced by integrating theoretical frameworks that resemble fuzzy sets, which better manage complexity uncertainty. integration reduces frequency of procedures, improving effectiveness decision-making. goal this work is present lower upper approximations hypersoft employ multi-argument-based parameters improve traditional sets soft sets. An intelligent mechanism decision assistance proposing a robust algorithm, based on proposed approximations. To validate prototype case study clinical diagnosis SCD discussed. criteria further refined using pertinent sub-criteria, such as functional ability, imaging data, neurological status criteria. Medical professionals would find suggested very helpful tool results indicate they could greatly contributes field medical diagnostics providing sophisticated multi-criteria analytical inherent ambiguity

Language: Английский

An intelligent multi-attribute decision-making system for clinical assessment of spinal cord disorder using fuzzy hypersoft rough approximations DOI Creative Commons

Muhammad Abdullah,

Khuram Ali Khan, Atiqe Ur Rahman

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 10, 2025

The data for diagnosing spinal cord disorder (SCD) are complex and often confusing, making it difficult established diagnostic techniques to yield reliable results. This issue frequently necessitates expensive testing get an accurate diagnosis. However, the process can be enhanced by integrating theoretical frameworks that resemble fuzzy sets, which better manage complexity uncertainty. integration reduces frequency of procedures, improving effectiveness decision-making. goal this work is present lower upper approximations hypersoft employ multi-argument-based parameters improve traditional sets soft sets. An intelligent mechanism decision assistance proposing a robust algorithm, based on proposed approximations. To validate prototype case study clinical diagnosis SCD discussed. criteria further refined using pertinent sub-criteria, such as functional ability, imaging data, neurological status criteria. Medical professionals would find suggested very helpful tool results indicate they could greatly contributes field medical diagnostics providing sophisticated multi-criteria analytical inherent ambiguity

Language: Английский

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