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Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

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Published on: December 13, 2024

Interpretable wrapper-based machine learning framework for predicting patellofemoral pain syndrome using minimal

Rajasekar Sannasi¹, Praveen Kumar Kandakurti², Thompson Stephan³

¹Department of Physiotherapy, College of Health Sciences, Gulf Medical University, Ajman, United Arab Emirates.

Scientific Reports

|December 12, 2025

Summary

A reduced set of 16 biomechanical features can accurately screen for patellofemoral pain syndrome (PFPS), maintaining high predictive power. This simplifies clinical assessments for anterior knee pain, improving efficiency for athletes and active individuals.

Keywords:

Classification Explainable AI Feature selection Interpretability Metaheuristic Musculoskeletal screening Patellofemoral pain syndrome Sports medicine Wrapper optimization

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Area of Science:

Biomechanics
Sports Medicine
Data Science

Background:

Patellofemoral pain syndrome (PFPS) is a common cause of anterior knee pain.
Current clinical screening involves numerous physical tests, which can be time-consuming.
The regional interdependence model highlights the complexity of biomechanical assessments.

Purpose of the Study:

To determine if a smaller subset of biomechanical variables can achieve high predictive accuracy for PFPS.
To develop a more efficient and clinician-interpretable screening tool.
To assess the impact of feature reduction on model performance and transparency.

Main Methods:

Analyzed biomechanical data from 70 participants with 29 features.
Employed nine classifiers and wrapper optimizers for feature selection.
Used logistic regression, comparing full-feature and reduced-feature models.
Applied explainable AI techniques (SHAP) for model interpretation.

Main Results:

A 16-feature model achieved 97.1% accuracy (AUC = 0.998), closely matching the full-feature model's 99.7% accuracy.
No significant loss in predictive performance was observed with feature reduction.
Key mobility angles (trunk rotation, lumbar extension, hip internal rotation, lumbar lateral flexion) were identified as dominant predictors.

Conclusions:

Wrapper-guided feature reduction effectively preserves predictive performance for PFPS screening.
A reduced biomechanical test battery can significantly shorten assessment time.
This approach offers a practical, cost-effective decision aid for PFPS screening, enhancing transparency and efficiency.