Neutrosophic AHP Method with Machine Learning Algorithms to The Priority of Maintenance in the Facility of Healthcare

Nihal N. Mostafa, Ibrahim Elhenawy
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Abstract

The creation of decision-support techniques that can be used in the planned preservation and recertification ordering of healthcare facility investments is regarded as an assignment of extremely high difficulty due to the multitude of ambiguity and levels of individuality that is accessible in a decision-making procedure of this nature. This research employs a mixture of Neutrosophic logic and the Analytical Hierarchical Process (AHP) to generate a trustworthy score of hospital structure facilities depending on their varying levels of evaluation and achievement deficiencies. This is done to reduce the partiality that is related to expert-driven choices and to make the rankings more objective. This is additionally merged with the innovative use of machine learning techniques in this field, specifically: Random Forest, and Naive Bayes, to automate the process of setting priorities and making it reproducible, thereby reducing the essential for extra professional decisions.
基于机器学习算法的中性粒细胞AHP方法在医疗设施维护优先级中的应用
决策支持技术的创建可用于医疗设施投资的计划保存和重新认证排序,这被认为是一项极其困难的任务,因为在这种性质的决策过程中存在大量的模糊性和个性化水平。本研究采用中性逻辑与层次分析法(AHP)的混合方法,根据医院结构设施的不同评估水平和成就缺陷,生成值得信赖的评分。这样做是为了减少与专家驱动的选择相关的偏见,并使排名更加客观。这还与该领域机器学习技术的创新使用相结合,特别是:随机森林和朴素贝叶斯,以自动设置优先级并使其可重复,从而减少额外专业决策的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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