## 4 Vibroarthrography of the Knee Joint
### Feature Extraction and Classification
#### 4.3.2 Classification  
The final step of VAG signal analysis is classification, the aim of which is to predict knee joint pathology by extracting important information from the dataset depending on its similarity to previously used examples. The classification process uses previously described parameters and statistical methods. An important aspect is their appropriate selection in order to maximise the accuracy of prediction. [PERSON] et al. [11] used the support vector machine (SVM) classification method to distinguish healthy knees from knees with OA. SVM is a single-layer and highly nonlinear model that is characterised by a higher generalisation ability in accurately classifying unknown data [134; 135]. They achieved a specificity of 0.75 and a sensitivity of 0.80. [PERSON] et al. [88] also used an SVM classifier and achieved a classification accuracy of 83.37% for the sit-stand-sit (SSS) movement and 85.74% for the swing-the-leg (SW) movement. They achieved comparable accuracy using the feedforward neural network (FFNN) classifier, which achieved results of 83.27% and 85.74% for SSS and SW movements, respectively. [PERSON] et al. [119] used an extension of the classic version of SVM, which is the least squares support vector machine (LS-SVM) and Bayesian decision rule (BDR). The overall accuracy (ACC) of the LS-SVM method was 82.67%. Significantly better results were observed when using the BDR VAG signal classification method, which uses information on feature density to distinguish normal VAG signals from those with pathological changes; an ACC of 88% was achieved. [PERSON] et al. [104] also used the LS-SVM classifier in their work. The classification was performed for five different feature sets. The lowest ACC of 74.19% was achieved for the following features: K, S, CWT 100-200 Hz, and CWT 200-500 Hz; whereas, the highest ACC of 86.77% was achieved for the feature set consisting of K, S,CWT 100-200 Hz, CWT 200-500 Hz, CWT 500-1000 Hz, and CWT 1000-2500 Hz. A higher classification result using the LS-SVM method was achieved by [PERSON] et al. [125]. An ACC of 91.01% was obtained using the genetic algorithm (four features), the use of all 24 features produced an ACC of 93.13%, while the use of the a priori algorithm resulted in an increase in ACC to 94.31%. In their study, they also used random forest (RF) as a classification method. This classification method is based on building multiple decision trees [136]. The classification accuracy using this classifier produced an ACC of 86.52% for all 24 features and the use of the a priori algorithm increased the ACC to 89.77%, while the genetic algorithm influenced the achievement of an ACC of 91.01%. The accuracy using the RF classifier achieved by the team of [PERSON] et al. [121] is definitely worth noting. The ACC achieved was in the range of 93-96%. For comparison, the accuracy result using SVM in their study was 79-95%. They also used the K-nearest neighbor (KNN) classification method, which is based on the neighbourhood of points in the feature space. The KNN classifier calculates a distance metric between the unlabelled and labelled feature vector data, then identifies the k-nearest objects [137,138]. The accuracy of this classifier reached \(>\) 90%. [PERSON] et al. [92] also used this method in their work and achieved an ACC of 87.25%. In addition, in their work, they used a logistic regression (LR) classifier whose ACC of 88% was similar to that achieved by KNN. Another classification method considered by [PERSON] et al. [108] is Naive Bayes (NB), which is based on [PERSON] theorem. It assumes that features are not dependent on each other. In the presented work, the accuracy of this classification method reached \(>\) 80%. In the analysed literature [76,77,85,87], classification methods such as a multi-layer perceptron (MLP) and radial basis function network (RBF) were also used. MLP is one of the most popular types of artificial neural networks, which is characterised by data flowing from input to output without feedback [139,140].