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Applying machine learning to gait analysis data for disease identification.
Ranveer Joyseeree1, Rami Abou Sabha2, Henning Mueller2
1Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland.
Studies in Health Technology and Informatics
|May 21, 2015
Summary
Machine learning accurately classifies patients with Neurological and Neuromuscular Diseases (NND) or Juvenile Idiopathic Arthritis (JIA) using gait analysis. This rapid gait analysis aids in disease identification and future clinical applications.
Area of Science:
- Computational biology and machine learning applications in clinical diagnostics.
- Biomechanical analysis and gait pattern recognition for disease classification.
Background:
- Diagnosing specific diseases within Neurological and Neuromuscular Diseases (NND) and Juvenile Idiopathic Arthritis (JIA) can be time-consuming.
- Gait analysis data holds potential for objective disease classification, improving diagnostic efficiency and data retrieval.
Purpose of the Study:
- To develop and assess a machine-learning framework for classifying patients into healthy, NND, or JIA categories using gait analysis data.
- To demonstrate the feasibility of using machine learning for gait-based disease classification, with future extensions to specific disease identification.
Main Methods:
- Collected standard clinical gait data from healthy individuals and patients with NND/JIA from the MD-PAEDIGREE initiative.
- Selected key gait parameters to train and evaluate Random Forest (RF), boosting, Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers.
- Employed cross-validation to rigorously test the classification performance of the developed machine learning models.
Main Results:
- Achieved 100% classification accuracy for RF, SVM, and MLP classifiers, and 96.4% for the boosting classifier.
- Demonstrated near-instantaneous training and testing times (milliseconds), indicating potential for real-time clinical applications.
- Identified the potential for high accuracy in distinguishing between healthy individuals and patients with NND or JIA based on gait.
Conclusions:
- Machine learning models, particularly RF, SVM, and MLP, can effectively classify patients into healthy, NND, and JIA groups using gait analysis data with high accuracy.
- The developed framework shows promise for rapid, real-time disease classification, potentially optimizing clinical workflows.
- Ongoing research focuses on refining feature selection and expanding datasets to enable classification of specific diseases within NND and JIA.
