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Summary

This study introduces a hybrid decision support system using fuzzy logic to assess the trustworthiness of visual analytics tools for medical data. This system enhances the reliability of AI-driven medical diagnoses and aids experts in selecting appropriate tools.

Keywords:
decision support systemsdecision-makingmedical data analysistrustworthy visualization tools

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Area of Science:

  • Medical Informatics
  • Data Science
  • Decision Support Systems

Background:

  • Electronic health records (EHRs) are crucial for disease diagnosis but raise concerns regarding data trustworthiness, privacy, and security.
  • Visual analytics offers a solution to information overload in medical data by integrating analytics with interactive visualizations.
  • Evaluating the trustworthiness of visual analytics tools in medical data analysis is complex, facing challenges in data processing, automation, and clarity of trustworthy relationships.

Purpose of the Study:

  • To develop a hybrid decision support system to assess and enhance the trustworthiness of visual analytics tools for medical data.
  • To address the lack of a hybrid system for evaluating visual analytics tool trustworthiness in medical data diagnosis.
  • To aid medical experts in selecting, evaluating, and ranking visual analytics tools for medical data analysis.

Main Methods:

  • Development of a hybrid decision support system utilizing fuzzy decision systems.
  • Implementation of a hybrid multi-criteria decision-making model based on the Analytic Hierarchy Process (AHP) and Similarity of Ideal Solutions (TOPSIS) in a fuzzy environment.
  • Comparison of proposed model results with accuracy tests and existing models.

Main Results:

  • The proposed hybrid decision support system effectively assesses and improves the trustworthiness of visual analytics tools for medical data.
  • The model demonstrates coherence and effectiveness through graphical interpretation and comparison analysis.
  • Accuracy tests showed high correlation, validating the proposed methodology.

Conclusions:

  • The developed system provides a robust method for evaluating visual analytics tool trustworthiness in medical diagnostics.
  • The study highlights the applicability of the proposed optimal decision-making model in real-world medical scenarios.
  • This research empowers medical professionals to make informed choices regarding visual analytics tools for improved patient care.