## 4 Results and Discussions
The 3 rd and 4 th best kappa value for LP-boost and total boost was 92.77% and 93.30%, respectively, and are increased from 92.37% to 95.71% and from 93.30% to 97.05% for both methods. The increasing results in all evaluation measures for the tenfold method makes the proposed results more promising for chest disease identification. We here provide a table of CNN activations of the five-fold technique and calculate the individual accuracy for each class. The results are shown in Table 8.  
The individual disease detection rate will lead us to propose a more confident model for particular disease detection. COVID-19 needs to be detected quickly and cheaply. In the proposed study, using the fivefold validation method showed that the best kappa value in the COVID-19 detection results was 99.0, i.e., the bag-ensemble. Similarly, for the other algorithms, i.e., the KNN-fine, LP-boost, and total-boost methods, the COVID-19 classification accuracy was 100%, 100%, and 98.0%, respectively. This shows that COVID-19 disease detection is highly accurate using the proposed framework. The COVID-19 results are set as a point of reference for investigating other chest disease detection results. Most of the results were detected with 100% or 99% accuracy. The best kappa value was attained with the bag-ensemble method. The all-class results were either 99% accurate or 100% accurate, but the Atelectasis disease classification was 97% accurate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Kappa (\%)** \\ \hline Bag-Ensemble & 99.77 & 99.77 & 99.98 & 99.73 & 99.74 & 97.86 \\ \hline KNN-coarse & 96.20 & 96.20 & 99.73 & 96.20 & 96.26 & 69.46 \\ \hline KNN-fine & 99.67 & 99.67 & 99.98 & 99.67 & 99.67 & 97.32 \\ \hline KNN-medium & 96.37 & 96.37 & 99.74 & 96.37 & 96.41 & 70.80 \\ \hline LP-Boost-Ensemble & 99.47 & 96.47 & 99.96 & 99.47 & 99.48 & 95.71 \\ \hline Subspace-discriminate & 96.53 & 96.53 & 99.75 & 96.53 & 96.56 & 72.14 \\ \hline Total-boost-Ensemble & 99.63 & 99.63 & 99.97 & 99.63 & 99.64 & 97.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification validation accuracy of tenfold validation using self-activated features. Similarly, the KNN-fine method was 100% accurate in COVID-19 detection, and Atelectasis detection accuracy was increased, as compared to the bag-ensemble method (97% to 99%). Emphysema, nodule, and pneumothorax disease results decreased from 100 to 98%, from 100 to 99%, and from 99 to 98%, respectively. The third best model in the fivefold method was the LP-boost method, which also achieves 100% COVID-19 detection results, where the Atelectasis disease results, as compared to the bag-ensemble method, increased from 97% to 100%. However, other classification results decreased slightly. For example, for Cardiomegaly, it decreased from 100 to 99%; for Nodule, it decreased from 100 to 97%; for Pleural Thickening, it decreased from 100 to 99%; and for pneumothorax, it decreased from 99 to 96.50%. However, it not only decreased the detection rate, but also increased in other disease detection results. The fourth best model, the total boost, also detected COVID-19 with 98% accuracy. It also decreased the classification results of Effusion, Emphysema, and Pneumothorax. In other disease detection results, it increased the detection rate, but it maintained results for some diseases. However, by looking into all ML classification methods, all performances are promising using the proposed method for multi-class chest disease detection. The ten-fold validation method proved to be more accurate than the fivefold method. This method based on individual class detection results are shown in Table 9.  
We can see in Table 9 that the COVID-19 detection accuracies achieved by the best algorithms showed 100%, 99%, 99%, and 99% for the bag-ensemble, KNN-fine, LP-boost, and total boost methods, respectively.