Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

中文翻译：

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通过基于犹豫模糊的混合医学专家系统分析数据可视化应用程序对诊断健康状况的影响

有效管理医疗数据对于准确诊断和个性化患者护理至关重要。随着医疗数据在个性化护理中的利用不断增长，人们对可靠性、隐私和安全性的担忧也随之出现。为了解决这些问题，本研究探索了分析技术与交互式视觉表示（称为视觉分析）的融合，作为一种有前途的解决方案。重点是评估沙特阿拉伯王国医疗保健数据的可信度，特别是可视化分析工具促进准确、安全的医疗保健数据分析的能力。这项研究解决了诸如缺乏具体评估标准、需要处理大量医疗数据集、建立信任以及自动化的必要性等挑战。为此，引入了混合医疗决策支持系统，利用犹豫模糊决策系统。主要目标是评估医疗数据中用于疾病诊断的视觉分析工具的可信度。在犹豫模糊逻辑的框架内，本文采用了一种医学多标准决策系统，该系统集成了分析网络过程和通过与理想解决方案相似的偏好顺序技术。严格的验证确保了研究结果的准确性和可靠性。该研究不仅提供了有价值的见解，而且还对所提出的模型与现有模型进行了比较分析，证明了最优决策在沙特阿拉伯医疗保健场景环境中的实用性。本研究中使用了几种流行的基于医疗保健的工具替代品，例如 Tableau、JupyteR、Zoho Reports、QlikView、Visual.ly、DOMO BI、SAS Visual Analytics。从所取得的结果来看，DOMO BI 可视化分析工具被认为是医疗保健专业人员最安全、最强大的工具。这项工作旨在加强患者护理和治疗结果，最终有助于改善沙特阿拉伯的整体医疗保健环境。