## 4 Results
### Prediction Performance  
The proposed ML methodology was applied on each of the five datasets. Specifically, the proposed FS was executed on the pre-processed versions of the datasets ranking the available features with respect to their relevance with the progression of OA. Then the proposed ML models were trained on feature subsets of increasing dimensionality (with a step of 5). These feature subsets were generated by sorting the features according to the selected ranking. This means that the proposed ML models were trained to classify KOA progressors and non-progressors based on the first (5, 10, 15, etc.) most informative features and the testing classification accuracies were finally calculated until the full feature set has been tested. The classification results on the five datasets are given below. * Dataset A  
Figure 7 depicts the testing performance (%) of the competing ML models with respect to the number of selected features for dataset A. In particular, DTs failed in this task, recording low testing performances (in the range of 42.44-65.85%). In contrast, the other models had an upward trend in the first 20-60 features, followed by a steady testing performance in most of the cases. Specifically, the logistic regression model showed an upward trend with respect to selected features in the first 30-50 features, with a maximum of 71.71% at 50 features (which was the overall best performer). The inclusion of additional features led to a small reduction in the accuracies achieved. Table 4 summarizes the results of logistic regression, XGboost, SVM, random forest, KNN, naive Bayes and DT on the two-class problem. A moderate number of features (in the range of 30-55) was finally selected by the majority of the ML models (in five out of the seven), whereas the overall maximum was achieved by LR on a group of fifty selected (50) risk factors. KNN and DTs selected more features (145 and 85, respectively) leading to low accuracies. The second highest accuracy was received for SVM and Naive Bayes (70.73% in both), whereas lower accuracies were obtained by NB, RF and XGboost. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Classes**} \\ \hline  & & Positive & Negative \\ \hline \multirow{2}{*}{Predicted classes} & Positive & True Positive & False Positive \\  & Negative & False Negative & True Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix. * Dataset B  
Figure 8 demonstrates the testing performance (%) of the competing ML models with respect to the number of selected features for dataset B. The following remarks could be extracted from Figure 8: (i) Considerably lower accuracies were achieved by all the competing ML models compared to the ones received in dataset A; (ii) LR and NB gave the maximum testing performance of approximately 64% at 25 features (which was the overall best performer in dataset B). The addition of more features did not increase the testing performance of the model but led to a reduction in the accuracies achieved. (iii) Low  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy (\%)** & \multicolumn{2}{c}{**Confusion Matrix**} & **Features** & **Parameters** \\ \hline \multirow{3}{*}{Logistic Regression} & \multirow{3}{*}{71.71} & A1 & A2 & \multirow{3}{*}{50} & \multirow{3}{*}{Penalty: 11, C: 1.0} \\  & & A2 & 30 & 74 & \\ \cline{3-5} \cline{5-5} \cline{  
testing performances were accomplished by the rest of the ML models (in the range of 42.24-62.11%). The accuracies and confusion matrixes reported in Table 5 verify the aforementioned results. In all the competing models, the best accuracies were recorded using a relatively small number of selected risk factors (less or equal to 40).