Open AccessDetection of Knee Joint Disorders using SVM Classifier
Author(s) -
S J Alphonsa Salu,
Jeraldin Auxillia D
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst218535
Subject(s) - pattern recognition (psychology) , principal component analysis , support vector machine , sample entropy , artificial intelligence , feature selection , entropy (arrow of time) , computer science , wavelet , classifier (uml) , feature extraction , wavelet transform , knee joint , approximate entropy , feature vector , mathematics , medicine , physics , quantum mechanics , surgery
A non-invasive technique using knee joint vibroarthographic (VAG) signals can be used for the early diagnosis of knee joint disorders. Among the algorithms devised for the detection of knee joint disorders using VAG signals, algorithms based on entropy measures can provide better performance. In this work, the VAG signal is preprocessed using wavelet decomposition into sub band signals. Features of the decomposed sub bands such as approximate entropy, sample entropy & wavelet energy are extracted as a quantified measure of complexity of the signal. A feature selection based on Principal Component Analysis (PCA) is performed in order to select the significant features. The extracted features are then used for classification of VAG signal into normal and abnormal VAG using support vector machine. It is observed that the classifier provides a better accuracy with feature selection using principal component analysis. And the results show that the classifier was able to classify the signal with an accuracy of 82.6%, error rate of 0.174, sensitivity of 1.0 and specificity of 0.888.