## 8 Results and Discussion
### Analysis of Classifier  
Table 2 describes the classification analysis of the adopted HEM-AL + DCNN scheme over traditional schemes.  
Figure 5: Performance of the developed scheme over extant schemes for “**(a)** FPR **(b)** FNR **(c)** FDR”.  
Figure 6: Performance of the proposed method over traditional models for **(a)** F1-score **(b)** MCC **(c)** NPV.  
On observing the analysis outcomes, the proposed HEM-AL + DCNN model has attained higher positive values for all metrics when compared to the existing schemes. On observing accuracy as shown in Table 2, the adopted scheme for test case 1 has attained a higher value (0.99), with 7.41%, 17.34% and 13.41% superiority to traditional NN, CNN and DCNN models. The sensitivity of the introduced scheme for test case 1 was found to be higher for the presented model. The F1-score of the suggested approach was 17.75%, 16.74% and 16.74% superior to traditional NN, CNN and DCNN models. Upon consideration of FPR, the developed scheme exhibited lower values than the extant approaches, thus ensuring the better development of the introduced model. The assessment proves the improvement of the suggested scheme for disease diagnosis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Test Case 1**} \\ \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**NN**} & \multicolumn{1}{c}{**CNN**} & \multicolumn{1}{c}{**DCNN**} & \multicolumn{1}{c}{**HEM-AL + DCNN without Optimal**} & \multicolumn{1}{c}{**HEM-AL + DCNN**} \\ \hline Sensitivity & 0.77 & 0.77667 & 0.77667 & 0.85671 & 0.90667 \\ Accuracy & 0.92195 & 0.8439 & 0.87317 & 0.94874 & 0.99024 \\ MCC & 0.44211 & 0.63158 & 0.56842 & 0.68912 & 0.72632 \\ F1-score & 0.78099 & 0.83173 & 0.81364 & 0.80876 & 0.88646 \\ FPR & 0.55789 & 0.36842 & 0.43158 & 0.34909 & 0.27368 \\ NPV & 0.84564 & 0.83777 & 0.84235 & 0.93548 & 0.93548 \\ Specificity & 0.42883 & 0.47967 & 0.46452 & 0.72323 & 0.78423 \\ FNR & 0.078049 & 0.1561 & 0.12683 & 0.06756 & 0.009756 \\ Precision & 0.44211 & 0.63158 & 0.56842 & 0.66182 & 0.72632 \\ FDR & 0.21901 & 0.16827 & 0.18636 & 0.15786 & 0.11354 \\ \hline \multicolumn{6}{c}{**Test Case 2**} \\ \hline \multicolumn{6}{c}{**Methods**} & \multicolumn{1}{c}{NN} & \multicolumn{1}{c}{CNN} & \multicolumn{1}{c}{DCNN} & \multicolumn{1}{c}{HEM-AL + DCNN without optimal} & \multicolumn{1}{c}{HEM-AL + DCNN} \\ \multicolumn{6}{c}{Specificity} & \multicolumn{1}{c}{0.88652} & \multicolumn{1}{c}{0.90094} & \multicolumn{1}{c}{0.90094} & \multicolumn{1}{c}{0.86007} & 0.90566 \\ Sensitivity & 0.86452 & 0.88136 & 0.84746 & 0.80533 & 0.85593 \\ F1-score & 0.91339 & 0.92553 & 0.96809 & 0.96809 & 0.96809 \\ Precision & 0.92414 & 0.93694 & 0.97087 & 0.97115 & 0.97115 \\ FPR & 0.086614 & 0.074468 & 0.031915 & 0.04115 & 0.031915 \\ Accuracy & 0.89333 & 0.9083 & 0.90498 & 0.89451 & 0.90991 \\ NPV & 0.77437 & 0.80259 & 0.81062 & 0.80123 & 0.81887 \\ FNR & 0.15148 & 0.13457 & 0.15254 & 0.15307 & 0.14407 \\ MCC & 0.91339 & 0.92553 & 0.96809 & 0.93809 & 0.96809 \\ FDR & 0.075862 & 0.063063 & 0.029126 & 0.031246 & 0.028846 \\ \hline \multicolumn{6}{c}{**Test Case 3**} \\ \hline \multicolumn{6}{c}{**Methods**} & \multicolumn{1}{c}{NN} & \multicolumn{1}{c}{CNN} & \multicolumn{1}{c}{DCNN} & \multicolumn{1}{c}{HEM-AL + DCNN without optimal} & \multicolumn{1}{c}{HEM-AL + DCNN} \\ \multicolumn{6}{c}{Precision} & \multicolumn{1}{c}{0.88889} & \multicolumn{1}{c}{0.88889} & \multicolumn{1}{c}{0.91667} & \multicolumn{1}{c}{0.92593} & 0.92593 \\ Sensitivity & 1 & 1 & 0.8 & 1 & 1 \\ FPR & 0.8 & 0.8 & 1 & 0.88667 & 0.86667 \\ Accuracy & 0.8 & 0.8 & 0.8456 & 0.80714 & 0.85714 \\ Specificity & 0.2 & 0.2 & 0.2321 & 0.16788 & 0.13333 \\ F1-score & 0.88889 & 0.88889 & 0.88889 & 0.92308 & 0.92308 \\ MCC & 0.8 & 0.8 & 0.83666 & 0.80879 & 0.86189 \\ FDR & 0 & 0 & 0.2 & 0 & 0 \\ NPV & 0.8 & 0.8 & 1 & 0.80123 & 0.86667 \\ FNR & 0.2 & 0.2 & 0 & 0.2096 & 0.14286 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification Performance of the adopted model and existing models for varied test cases.