Fuzzy Rules and Genetic Algorithms Team Up to Build a Digital Twin for Multiple Sclerosis

Multiple sclerosis is one of medicine’s most stubborn forecasting problems. The disease unfolds differently in every patient, disability accumulates in bursts and plateaus, and the clinical records that neurologists rely on are notoriously sparse and irregular. A research team from King Fahd University of Petroleum and Minerals and King Fahd Specialist Hospital in Dammam, Saudi Arabia, now proposes a way to turn that messy reality into something a computer can reason about transparently. Writing in the journal Applied Intelligence, they describe a predictive engine built from genetic fuzzy logic that could serve as the computational heart of a digital twin for multiple sclerosis patients.

Digital twins, virtual replicas of physical systems that update in real time as new data arrive, have transformed manufacturing and are steadily migrating into healthcare. In cardiology, diabetes management and pathology, researchers are building patient-specific models that can simulate how a disease might evolve under different treatment scenarios. But a clinical digital twin is only as good as its predictive core, and that core faces a dilemma familiar to anyone working in medical artificial intelligence: the most accurate models are often the least explainable, and the most explainable models are often the least accurate.

The new framework, called MiG-FRBS, short for Michigan-style Genetic Fuzzy Rule-Based System, attempts to resolve that tension by design. Fuzzy logic, a mathematical approach that handles uncertainty through degrees of truth rather than binary yes-or-no categories, produces rules that read almost like clinical reasoning. A rule might state that if a patient’s pyramidal subscore is moderately elevated and their cerebellar function is slightly impaired, then their overall disability is likely to increase. Genetic algorithms, optimization techniques inspired by natural selection, then evolve and refine these rules automatically, breeding generations of candidate rules and keeping only those that best fit the data.

The Michigan-style architecture, named after the approach pioneered at the University of Michigan in the 1970s, encodes each fuzzy rule as an individual organism in the evolving population. This contrasts with Pittsburgh-style systems, where entire rule sets compete as units. The Michigan design allows the algorithm to discover a diverse collection of complementary rules, each covering a different region of the patient data, which naturally produces compact and human-readable rule bases rather than sprawling libraries of overlapping logic.

One of the study’s key technical innovations is its treatment of time. Multiple sclerosis patients typically visit clinics at irregular intervals, sometimes months apart, and their records capture snapshots rather than continuous trajectories. To bridge these gaps, the researchers applied spline-based interpolation, a mathematical technique that fits smooth polynomial curves through known data points, generating synthetic time-series data that allows the model to forecast continuously rather than only at visit dates. The framework accepts both raw measurements and differenced inputs, the latter capturing changes between consecutive time points, which helps the system detect trends and rates of deterioration rather than absolute levels.

Another structural choice sets the system apart from conventional forecasting models. Instead of predicting a single aggregate disability score, MiG-FRBS employs a Multiple Input Multiple Output, or MIMO, architecture that predicts several functional subscores of the Expanded Disability Status Scale simultaneously. The EDSS, the standard clinical measure of MS disability, decomposes into subscores covering distinct neurological systems, including pyramidal, cerebellar, brainstem, sensory, bowel and bladder, visual, cerebral and ambulation functions. By forecasting these components in parallel, the model builds a richer virtual representation of a patient’s overall condition, capturing the fact that disability in MS does not progress uniformly across all systems.

The choice of Gaussian membership functions, smooth bell-shaped curves that define how strongly a given value belongs to a fuzzy category, reflects both mathematical and practical considerations. Gaussian functions are continuous, symmetric and differentiable everywhere, which avoids the abrupt boundaries that plague triangular or trapezoidal membership functions and produces more stable inference when inputs shift slightly. This smoothness matters in a clinical setting, where a small change in a measured value should not cause the model’s output to jump discontinuously.

When evaluated on real patient data from King Fahd Specialist Hospital, the framework delivered promising results while preserving interpretability. Predictions of mental subscores achieved accuracies above 90 percent, while brainstem and bowel subscores exceeded 80 percent accuracy. Those figures are notable not despite the model’s transparency but arguably because of its design philosophy. Rather than fitting thousands of opaque parameters, the system distills its knowledge into a small set of succinct rules that clinicians can inspect, question and trust. The authors argue that this positions MiG-FRBS as the virtual modelling layer of a digital twin, an engine that forecasts disease progression in a form that supports, rather than replaces, clinical judgment.

The work arrives amid a wave of machine learning efforts to predict MS progression. Recent studies have applied deep learning architectures, including LSTM networks, to forecast five-year EDSS scores, and neural networks trained on routinely collected healthcare data to estimate disability. Explainable AI methods have been used to identify which baseline MRI features predict disease trajectory. Yet most of these approaches treat interpretability as an afterthought, applying post-hoc explanation tools such as SHAP values to models that were never designed to be understood. The genetic fuzzy approach inverts that logic, building explainability into the model’s very structure.

Significant hurdles remain before such a system reaches the clinic. The training data came from a single hospital and are not publicly available, raising questions about generalizability across populations and healthcare settings. Sparse clinical records, even with interpolation, may not capture the full complexity of disease dynamics that continuous monitoring could reveal. And any digital twin deployed in practice would need rigorous prospective validation, demonstrating that its forecasts improve outcomes rather than merely match retrospective data. Still, the study offers a concrete template for how interpretable, evolutionarily optimized models could anchor the next generation of personalized neurological care, where a patient’s digital counterpart evolves alongside them, rule by transparent rule.

Subject of Research: A genetic fuzzy rule-based system for predicting multiple sclerosis disability progression as the predictive core of a clinical digital twin

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

Article References: Faisal, M., Ahmed, M., Khan, S., & Bunyan, R. F. (2026). A Michigan style genetic fuzzy system framework for multiple sclerosis: towards a digital twin application. Applied Intelligence, 56(16), Article 497. https://doi.org/10.1007/s10489-026-07545-y

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07545-y

Keywords: multiple sclerosis, digital twin, genetic fuzzy systems, fuzzy logic, genetic algorithms, EDSS, interpretable machine learning, time series forecasting, clinical decision support, spline interpolation, predictive modeling, healthcare AI