## 4 Results
### Prediction Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **Confusion Matrix** & **Features** & **Parameters** \\ \hline Logistic & & & B1 & B2 & & \\ Regression & 63.98 & B1 & 48 & 22 & 25 & Penalty: 11, C: 1.0 \\  & & B2 & 36 & 55 & & \\  & & B1 & B2 & & & \\ Naive Bayes & 63.98 & B1 & 50 & 20 & 35 & GaussianNB \\  & & B2 & 38 & 53 & & \\  & & B1 & B2 & & & \\ SVM & 61.49 & B1 & 46 & 24 & 35 & C: 6, kernel: linear \\  & & B2 & 38 & 53 & & \\  & & B1 & B2 & & & leaf\_size: 1, n\_neighbors: 16, \\ KNN & 57.76 & B1 & 63 & 7 & 15 & weights: uniform \\  & & B2 & 61 & 30 & & \\ Decision & & & B1 & B2 & & \\ Tree & 58.39 & B1 & 41 & 29 & 15 & max\_features: auto, min\_samples\_leaf: \\  & & B2 & 38 & 53 & & 1, min\_samples\_split: 6 \\ Random & & & B1 & B2 & & \\ Forest & 62.11 & B1 & 48 & 22 & 15 & criterion: gini, min\_samples\_leaf: 2, \\  & & B2 & 39 & 52 & & min\_samples\_split: 7, n\_estimators: 30 \\  & & & B1 & B2 & & \\ XGboost & 60.25 & B1 & 44 & 26 & 40 & gamma: 0.4, max\_depth: 7, \\  & & B2 & 38 & 53 & & min\_child\_weight: 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best testing accuracies achieved for each ML model along with the confusion matrix, the optimum number of features and the hyperparameters of the ML models employed. B1 and B2 denote classes 1 and 2 of dataset B, respectively. Figure 8: Learning curves with testing accuracy scores on dataset B for different ML models trained on feature subsets of increasing dimensionality. * Dataset D  
The combination of datasets A and B proved to be beneficial in the task of predicting KOA progression. Specifically, the following conclusions are drawn from the results reported in Figure 10 and Table 7: (i) The best performance (74.07%) was achieved by the SVM on the group of the fifty-five selected risk factors with linear kernel penalty and C = 0.1 (Dataset D). This performance was the overall best one achieved in all five datasets. (ii) The second highest accuracy was received for the logistic regression (72.84%), whereas lower accuracies were obtained by the rest of the models. (iii) SVM and LR followed a similar progression in the reported accuracies with respect to the number of selected features with an upward trend in the first 20-55 features, followed by a slight performance decrease as the number of features increases. (iv) KNN gave moderate results with a maximum testing performance of 71.6% at 75 selected features. (v) Low testing accuracies were obtained by RF, XGboost and DT in the range of 42.59-66.67%. * Dataset E  
In dataset E, the SVM-based approach exhibited an upward trend with respect to selected features in the first 20-70 features, with a maximum of 71.81% at 70 features (which was the best in the category). The inclusion of additional features led to a small reduction in the accuracies achieved (Figure 11). Similarly to SVM, LR gave the second highest accuracy (71.14%) for less features (55). XGboost also gave a comparable performance (70.47%) in a subset of 45 selected features. Lower testing accuracies were received by the rest of ML models (Table 8). Table 9 cites the best accuracies achieved in each of the five datasets. The combined effect of baseline features (dataset A) and progression data \(dx^{1}_{i,j}\) (dataset B) had a positive effect on the prediction capacity of the proposed methodology, as clearly shown in Table 7 where the testing accuracy in dataset D is increased by 2.36% compared to the result obtained in dataset A. A minor difference (0.1%) is observed on the accuracies reported for datasets A and E, demonstrating that \(dx^{2}_{i,j}\) progression data have a negligible effect on the predictive capacity of the proposed methodology and therefore could be omitted. The accuracies received in datasets B and C reveal that the baseline features are crucial for predicting KOA progression.