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<p>Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a&#xa0;simulated dataset&#xa0;of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.</p>

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A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月16日

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  • Mohammed S. Al-Samarraay,
  • Aws A. Magableh,
  • Rana I. Mahmood,
  • Shahad Sabbar Joudar,
  • Idrees A. Zahid,
  • Jameel R. Al-Obaidi,
  • Ali Z. Al-Saffar,
  • A. S. Albahri,
  • O. S. Albahri,
  • A. H. Alamoodi,
  • Mohd Faizal Nizam Lee Abdullah

摘要

Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a simulated dataset of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.