A Michigan style genetic fuzzy system framework for multiple sclerosis: towards a digital twin application

Abstract

Digital Twin (DT) technology is increasingly being explored in healthcare to support personalized monitoring and decision-making. A critical requirement of any clinical DT is an interpretable and reliable predictive model that can represent the evolving condition of individual patients. This study presents a Michigan-style Genetic Fuzzy Rule-Based System (MiG-FRBS) developed as the predictive analytics core of a DT for Multiple Sclerosis (MS). The proposed framework is designed to convert sparse, irregular clinical records into meaningful, explainable forecasts of disease progression. MiG-FRBS combines fuzzy logic with genetic algorithms to automatically generate compact and human-readable rules. Unlike conventional black-box Machine Learning (ML) models, the system provides transparent reasoning by identifying key features, temporal patterns, and decision rules that describe changes in Expanded Disability Status Scale (EDSS) functional subscores. A Multiple Input Multiple Output (MIMO) structure is employed to predict several subscores simultaneously, enabling a more comprehensive virtual representation of patient status. Spline-based interpolation was used to create synthetic time-series data to allow continuous forecasting because Ms data are usually sparse and unevenly distributed throughout time. The framework accepts both raw and differenced inputs and makes use of Gaussian membership functions. With accuracies above 90% for Mental scores and above 80% for Brainstem and Bowel scores, experimental evaluation on actual MS patient data showed promising performance while preserving good interpretability through compact, succinct rule bases. In order to assist future data-driven clinical decision support for MS management, the suggested MiG-FRBS model offers an adaptable and transparent forecasting engine that can function as the virtual modelling layer of a DT.