An Interpretable Rule-Based Clinical Decision Support Framework for Electrotherapy-Related Conditions using Simulink

Electrotherapy techniques, including Transcutaneous Electrical Nerve Stimulation (TENS) and Neuromuscular Electrical Stimulation (NMES), are extensively utilized in physiotherapy for pain alleviation and musculoskeletal rehabilitation. Treatment selection frequently relies on expert clinical judgment, which may not be uniformly accessible across all healthcare environments. This research introduces a comprehensible, rule-based mathematical model for disease identification, executed in MATLAB/Simulink, to categorize clinical situations pertinent to electrotherapy. The model correlates validated clinical inputs—Visual Analog Scale (VAS), symptom duration, DN4 score, muscle strength percentage, range of motion (ROM), swelling, and age—into seven output categories: acute pain, chronic pain, neuropathic pain, muscle weakness, soft tissue injury, osteoarthritis, and absence of disease. The innovation of the approach is the explicit operationalization of clinically recognized criteria into transparent decision equations that can be simulated and audited within Simulink. A synthetic dataset was created to verify the rule logic. Through confusion-matrix-based evaluation, the model attained an overall accuracy of 47.5% on the synthetic dataset, suggesting that symptom overlap, and threshold closeness may result in misclassification inside deterministic rule systems. These findings validate the model as a proof- of-concept decision-support framework and encourage the modification of rule priorities and further validation using actual clinical datasets. This work presents a proof-of-concept framework validated on a synthetic dataset, intended for future clinical integration.