Interpretable Support Vector Machine and Its Application to Rehabilitation Assessment

This paper does present an interpretable support vector machine (SVM) and its application to rehabilitation assessment. We introduce the concept of nearest boundary point to standardize the one-class SVM decision function and determine the shortest path for data from abnormal cases to become those f...

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Bibliographic Details
Published in:Electronics (Basel) 2024-09, Vol.13 (18), p.3584
Main Authors: Kim, Woojin, Joe, Hyunwoo, Kim, Hyun-Suk, Yoon, Daesub
Format: Article
Language:English
Subjects:
Classification
Data analysis
Datasets
Medical research
Medicine, Experimental
Methods
Muscle function
Normal distribution
Patients
Rehabilitation
Simulation
Support vector machines
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Summary:This paper does present an interpretable support vector machine (SVM) and its application to rehabilitation assessment. We introduce the concept of nearest boundary point to standardize the one-class SVM decision function and determine the shortest path for data from abnormal cases to become those from normal cases. This analytical approach is computationally simple and provides a unique solution. The nearest boundary point of abnormal data can also be used to analyze the cause of abnormal classification and indicate countermeasures for normalization. These properties render the proposed interpretable SVM valuable for medical assessment applications and other problems that require careful consideration of classification results for treatment. Simulation and application results demonstrate the feasibility and effectiveness of the proposed method.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13183584