Abstract:
Human Body joints are vital for normal body movements. Early diagnosis of any problem in
joints helps the doctors for the timely treatment of the diseases. To identify these problems,
many diagnosis methods are available but the easiest and most effective way is through
Vibroarthrography. Vibroarthrography is the method of detecting the vibration signals from the
knee joint to diagnose any disorders in it. Researchers are studying the usage of vibration
signals from the human knee joints, known as Vibroarthrographic (VAG) Signals, for the
diagnosis of the condition of the knee joint. There are various types of features and classifiers
used for the classification of VAG signals into normal and abnormal signals. In this research,
different types of features of the time domain and spectral domain are explored, and studied the
combination of these features. These features include statistical features, Auto-Encoder Based
features, and Continuous Wavelet Transform based features. The features are then selected by
correlation coefficients and fed into classifiers models. Different classifiers are examined but
the best results have been achieved by using the Decision Tree Classifier. The accuracy
achieved using the Decision Tree Classifier is 93.26%. We have concluded that the proposed
methodology performed very well in other performance evaluation parameters as well. We
achieved the Sensitivity of 86.84%, Specificity of 98.04%, PPV of 97.06%, NPV of 90.91%,
and a Matthews Correlation Coefficient (MCC) score of 0.8641. The proposed method has Area
under the Curve of ROC approximately equal to 0.91. The proposed methodology gives us
more accurate results, as compared to previous researches, without going into the Deep learning
methods that are complex and time-consuming.
