A Fuzzy Multi-Criteria Decision-Making Approach to Personalised Treatment in Neuroscience

Abstract

Personalised treatment approaches have become increasingly important in the field of neuroscience, aiming to improve healthcare quality and patient satisfaction. This study contributes to the development of personalised treatment strategies by integrating fuzzy multi-criteria decision-making (FMCDM) techniques. Given the uncertainties and multidimensional criteria involved in treatment evaluations, FMCDM provides a robust framework to enhance decision-making in healthcare. The primary objectives of this study are to manage uncertainties in treatment evaluations, to develop an integrated decision-making approach for patient monitoring, and to evaluate treatment criteria on an individual patient basis. The prioritization of treatment criteria for each patient was performed using Fuzzy Logarithm Methodology of Additive Weights (FLMAW), while personalised treatment approaches were evaluated with Fuzzy Ranking of Alternatives with Weights of Criteria (FRAWEC) method. The results demonstrated that the criteria for Patient A and Patient B, as recommended by the expert team, were distinct, leading to different personalised treatment approaches for each. The proposed model integrates FLMAW and FRAWEC methods to optimise personalised treatment strategies by addressing uncertainties and evaluating key factors such as biometric data, treatment response, and psychosocial aspects. By prioritising treatment criteria and ranking interventions based on individual patient profiles, the model facilitates tailored treatment plans that address both physical and psychological health needs. This study discusses practical implications for healthcare professionals and management strategies for implementing these innovative approaches. Future research highlights the need for broader expert collaboration and the continuous integration of advanced technologies to update monitoring criteria.