Ann Rehabil Med.  2020 Dec;44(6):415-427. 10.5535/arm.20071.

Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

• Yang JH
• Park JH
• Jang SH
• Cho J

• KMID: 2510809
• DOI: http://doi.org/10.5535/arm.20071

Abstract

Objective
To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.
Methods
A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.
Results
In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.
Conclusion
The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

Keyword

Knee osteoarthritis; Gait analysis; Knee joint; Severity; Machine learning

Figure

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  Fig. 1. A hardware of the gait analysis system consisting of PC and wireless IMU sensors (A), and gait trial of knee osteoarthritis patient (B) and normal subject (C). IMU, inertial measurement unit.

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  Fig. 2. The gait waveform data of one representative normal subject’s lower limb was divided and expressed by each segmented gait phase.

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  Fig. 3. Conceptualization of the support vector machine (SVM). The purpose of the SVM is to identify a hyperplane that precisely divides the data points. A hyperplane is a decision boundary that segregates data into classes. The data points close to the hyperplane are support vectors, and these affect the position of the hyperplane.

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  Fig. 4. Flow diagram for the classification algorithm of knee osteoarthritis patients. SVM, support vector machine.

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  Fig. 5. Flow diagram of feature selection for improving the SVM classification of knee osteoarthritis severity. SVM, support vector machine; OA, osteoarthritis.

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  Fig. 6. The results of classification of knee osteoarthritis (OA) patients and normal subjects using an SVM classifier as a confusion matrix. The number of correct predictions is 75 and 93 for true positive and negative cases, respectively; and the number of incorrect predictions is 4 and 9 for false positive and negative cases, respectively. SVM, support vector machine.

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  Fig. 7. The results of knee osteoarthritis severity classification using the SVM classifier as a confusion matrix. The number of correct predictions is colored green in 4×4 confusion matrix. SVM, support vector machine.

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  Fig. 8. The accuracy of knee osteoarthritis severity classification according to the number of features selected by the ReliefF algorithm.

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  Fig. 9. The difference in waveform data according to the severity of knee osteoarthritis in the sagittal plane of the knee joint from one representative subject in each group.

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