## 4 Results and Discussions
We can set the bag-ensemble algorithm results as a frame of reference for disease detection. The other algorithms showed that other methods are 1% less accurate than the bag-ensemble method. This method also achieves the highest confidence value of kappa. This algorithm not only achieves 100% for COVID-19 detection but also increases the classification accuracy for Atelectasis to 99%, which was 97% in the fivefold method. Furthermore, it also increased the detection results for fibrosis, mass, pneumonia, and pneumothorax from 99% to 100%. However, it decreased \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Bag-** & **KNN-** & **KNN-** & **KNN-** & **LP-Boost** & **Subspace-** & **Total-Boost** \\  & **Ensemble** & **Coarse** & **Medium** & **Ensemble** & **Discriminant** & **Ensemble** \\ \hline Atelectasis & 97.00 & 95.0 & 99.0 & 94.0 & 100 & 88.0 & 97.0 \\ \hline Cardiomegaly & 100 & 99.0 & 100 & 99.95 & 99.0 & 99.0 & 99.0 \\ \hline Consolidation & 99.0 & 97.0 & 100 & 97.0 & 99.0 & 94.0 & 100 \\ \hline COVID-19 & 99.0 & 96.0 & 100 & 97.5 & 100 & 90.0 & 98.0 \\ \hline Edema & 99.0 & 97.50 & 99.0 & 98.0 & 100 & 97.0 & 100 \\ \hline Effusion & 100 & 89.0 & 100 & 90.0 & 100 & 89.0 & 97.0 \\ \hline Emphysema & 100 & 96.0 & 98.0 & 99.6 & 100 & 95.0 & 98.50 \\ \hline Fibrosis & 99.0 & 99.0 & 99.0 & 98.0 & 99.0 & 98.0 & 100 \\ \hline Hernia & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline Infiltration & 100 & 96.0 & 100 & 96.0 & 100 & 94.0 & 100 \\ \hline Mass & 99.0 & 97.0 & 100 & 97.0 & 99.0 & 89.0 & 100 \\ \hline Nodule & 100 & 94.0 & 99.0 & 95.0 & 97.0 & 88.0 & 100 \\ \hline Pleural Thickening & 100 & 99.0 & 100 & 99.0 & 99.0 & 92.0 & 100 \\ \hline Pneumonia & 99.0 & 99.0 & 99.0 & 99.0 & 98.0 & 100 & 100 \\ \hline Pneumothorax & 99.0 & 89.0 & 98.0 & 88.5 & 96.50 & 88.0 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Self-activated features based on classification using the fivefold technique results on individual class accuracy. detection of infiltration from 100% to 98%. This disease detection decrease is negligible if we look upon the improvement of the other six diseases. However, the best results achieved by the tenfold method makes the detection rate of all diseases 100%, except for three, two of which (Atelectasis and Consolidation) are 99% accurate and one is 98% (Infiltration). Furthermore, the 2 nd best method also improved the results of detection for all diseases. It ranges from 99 to 100%. The 10 classes reach 100% (cardiomegaly, consolidation, edema, emphysema, fibrosis, hernia, mass, nodule, pleural thickening, and pneumonia) accuracy, where five (atelectasis, COVID-19, effusion, infiltration, and pneumothorax) reach 99%, so these results are improved compared to the fivefold validation approach. The 3\({}^{rd}\) best model, LP-boost, also increases its detection results and reaches 100 detections in eight diseases (cardiomegaly, edema, fibrosis, hernia, infiltration, mass, nodule, and pleural-thickening), 99% for five diseases (atelectasis, consolidation, COVID-19, pneumonia, and pneumothorax), and 98% for two diseases (effusion and emphysema). The 4\({}^{th}\) best model also improved its results by giving a 100% accurate detection for nine diseases (cardiomegaly, consolidation, edema, emphysema, fibrosis, hernia, infiltration, pleural thickening, and pneumonia) and 99% for six other diseases (atelectasis, COVID-19, effusion, mass, nodule, and pneumothorax). It is observed that the tenfold method shows the less accurate results. The kappa confidence value is improved in the tenfold method. Therefore, tenfold cross-validation is a more robust, confident, promising method to classify chest diseases using more randomization and folds. The comparison of state-of-the-art studies on COVID-19 disease detection and other chest diseases are shown in the next section.