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

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.