Open AccessFeature Extraction and Classification for the Detection of Knee Joint Disorders using Random Forest Classifier
Author(s) -
Alphonsa Salu,
Jeraldin Auxillia
Publication year - 2021
Publication title -
international journal of emerging trends in engineering research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 14
ISSN - 2347-3983
DOI - 10.30534/ijeter/2021/099102021
Subject(s) - pattern recognition (psychology) , random forest , principal component analysis , sample entropy , artificial intelligence , feature selection , feature extraction , classifier (uml) , wavelet , entropy (arrow of time) , computer science , support vector machine , mathematics , physics , quantum mechanics
A non-invasive technique using knee joint vibroarthrographic (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 and 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 random forest classifier. It is observed that the classifier provides a better accuracy with feature selection using principal component analysis. And the result shows that the classifier is able to classify the signal with an accuracy of 87%, error rate of 0.13, sensitivity of 0.874 and specificity of 0.777.