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Abstract
Although Reliability Block Diagrams (RBDs), are frequently employed for reliability analysis, their efficacy in healthcare applications is limited because they frequently fail to address the imprecision and uncertainty present in clinical data. By adding Neutrosophic Logic to RBDs, this work creates a novel method for improving reliability evaluations in ambiguous clinical settings. In contrast to conventional approaches, the Neutrosophic RBD model better represents the complexity of healthcare systems by integrating truth, indeterminacy, and falsity components. R programming is used to develop the model, and simulations are used to gauge how well it performs in various clinical settings. According to the simulations, the Neutrosophic RBD model outperforms conventional RBDs in managing uncertainty and enhancing decision-making, offering more flexibility and more precise reliability evaluations. This research establishes a foundation for future research that could broaden the application of this method to other decision-support systems, both within and beyond the healthcare sector.
Article highlights
By incorporating Truth, Indeterminacy, and Falsity components, Neutrosophic logic enhances evaluation of complex health metrics such as blood pressure, cholesterol, etc, of a patient's health state by providing nuanced insights into variations, than the binary classifications as given by fuzzy logic.
Reliability Block Diagrams (RBDs) and Neutrosophic logic can be combined to enhance the ability to handle ambiguous and partial clinical data and thus provides more accurate reliability evaluations in situations where device reliability or health statistics are unknown.
Learners gain insights into analyzing complex health data with precision, while researchers can develop advanced models integrating neutrosophic logic and machine learning to improve healthcare prognosis. Case studies further establish its practical applications and open avenues for future exploration.