Evidence map›Paper›PMID 40866354›Full record

ArticleScientific reports2025

Analysis of anti-cancer treatment therapies based on TOPSIS method under fractional Diophantine fuzzy Muirhead mean operators.

Abbas Qadir, Saleem Abdullah, Ariana Abdul Rahimzai, Saifullah Khan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Abbas QadirDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.
Saleem AbdullahDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.
Ariana Abdul RahimzaiDepartment of Mathematics, Education Faculty, Laghman University, Mehterlam city, Laghman, 2701, Afghanistan. ariana.abdulrahimzai@lu.edu.af.
Saifullah KhanDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer affects millions of lives worldwide each year, and finding the most effective treatment is a difficult and important decision for healthcare professionals. This research aims to develop a new method for selecting the most appropriate and affordable cancer treatments. For this purpose, fractional Diophantine fuzzy sets (FDFSs) and the Muirhead mean operator are introduced to deal with uncertainty in medical decision-making. A difficult problem in decision-making is to detect the hidden weight information of criteria and decision-making experts. To solve this problem, the analytical hierarchy process (AHP) is used to calculate unknown weights for both criteria and decision-making experts. Furthermore, a fractional Diophantine fuzzy-TOPSIS (FDF-TOPSIS) technique is proposed to evaluate and select the best treatment process. Next, the proposed FDF-TOPSIS technique is applied to a real-life numerical example of finding the optimal cancer treatment process. Finally, we compare our proposed method with other existing methods and demonstrate the reliability, accuracy, and feasibility of the proposed technique, highlighting its potential to improve decision-making during cancer treatment.

Indexed as

Clinical Decision-MakingFuzzy LogicNeoplasmsAlgorithmsHumansAnalytical hierarchical processFractional Diophantine fuzzy setMuirhead mean operatorsTOPSIS method

Identifiers

PMID40866354
PMCPMC12391464

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