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Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data securi...

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Detalles Bibliográficos
Autor principal: Albarrak, Abdullah M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253023/
https://www.ncbi.nlm.nih.gov/pubmed/37296769
http://dx.doi.org/10.3390/diagnostics13111916
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author Albarrak, Abdullah M.
author_facet Albarrak, Abdullah M.
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description Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data.
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spelling pubmed-102530232023-06-10 Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System Albarrak, Abdullah M. Diagnostics (Basel) Article Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data. MDPI 2023-05-30 /pmc/articles/PMC10253023/ /pubmed/37296769 http://dx.doi.org/10.3390/diagnostics13111916 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Albarrak, Abdullah M.
Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
title Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
title_full Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
title_fullStr Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
title_full_unstemmed Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
title_short Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
title_sort determining a trustworthy application for medical data visualizations through a knowledge-based fuzzy expert system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253023/
https://www.ncbi.nlm.nih.gov/pubmed/37296769
http://dx.doi.org/10.3390/diagnostics13111916
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