# Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
## 4 Results and Discussion
### Comparative Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Methods**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**NB**} & \multirow{2}{*}{**DBN**} & \multirow{2}{*}{**Proposed Taylor-BSA–DBN**} \\ \cline{3-3} \cline{5-6}  & \multicolumn{2}{c}{**k-Fold**} & & & & \\ \hline \hline \multirow{4}{*}{Accuracy} & 5 & 0.7021 & 0.7088 & 0.7126 & 0.7239 \\  & 6 & 0.7122 & 0.7189 & 0.7245 & 0.7365 \\  & 7 & 0.7345 & 0.7543 & 0.7634 & 0.7843 \\  & 8 & 0.7528 & 0.7843 & 0.7965 & 0.8132 \\ \hline \multirow{4}{*}{Sensitivity} & 5 & 0.7567 & 0.7678 & 0.7898 & 0.7956 \\  & 6 & 0.7834 & 0.8045 & 0.8156 & 0.8232 \\  & 7 & 0.8032 & 0.8189 & 0.8256 & 0.8321 \\  & 8 & 0.8145 & 0.8229 & 0.8365 & 0.8448 \\ \hline \multirow{4}{*}{Specificity} & 5 & 0.7586 & 0.7656 & 0.7699 & 0.7865 \\  & 6 & 0.7854 & 0.7745 & 0.7965 & 0.8043 \\  & 7 & 0.7940 & 0.8088 & 0.8124 & 0.8227 \\  & 8 & 0.8021 & 0.8178 & 0.8249 & 0.8339 \\ \hline \multicolumn{6}{c}{Hungarian} \\ \hline \multirow{4}{*}{Accuracy} & 5 & 0.7960 & 0.8758 & 0.8791 & 0.8822 \\  & 6 & 0.8071 & 0.8413 & 0.8838 & 0.8854 \\  & 7 & 0.7985 & 0.8030 & 0.8324 & 0.8917 \\  & 8 & 0.7982 & 0.8626 & 0.8948 & 0.9021 \\ \hline \multirow{4}{*}{Sensitivity} & 5 & 0.7959 & 0.8027 & 0.8197 & 0.8231 \\  & 6 & 0.7857 & 0.7891 & 0.7925 & 0.8367 \\  & 7 & 0.7891 & 0.7925 & 0.7959 & 0.8393 \\  & 8 & 0.7857 & 0.7925 & 0.8061 & 0.8458 \\ \hline \multirow{4}{*}{Specificity} & 5 & 0.7494 & 0.7513 & 0.7645 & 0.7656 \\  & 6 & 0.7146 & 0.7343 & 0.7645 & 0.7760 \\  & 7 & 0.7433 & 0.7535 & 0.7645 & 0.7719 \\  & 8 & 0.7530 & 0.7645 & 0.7697 & 0.7873 \\ \hline \multicolumn{6}{c}{Switzerland} \\ \hline \multirow{4}{*}{Accuracy} & 5 & 0.7528 & 0.7789 & 0.7799 & 0.7896 \\  & 6 & 0.7658 & 0.7828 & 0.7925 & 0.8012 \\  & 7 & 0.7712 & 0.7958 & 0.8028 & 0.8156 \\  & 8 & 0.7828 & 0.8128 & 0.8159 & 0.8259 \\ \hline \multirow{4}{*}{Sensitivity} & 5 & 0.7428 & 0.7689 & 0.7847 & 0.7956 \\  & 6 & 0.7625 & 0.7758 & 0.7858 & 0.8028 \\  & 7 & 0.7748 & 0.7896 & 0.7952 & 0.8125 \\  & 8 & 0.7828 & 0.7986 & 0.8078 & 0.8225 \\ \hline \multirow{4}{*}{Specificity} & 5 & 0.7658 & 0.7589 & 0.7750 & 0.7896 \\  & 6 & 0.7758 & 0.7832 & 0.7962 & 0.8020 \\ \cline{1-1}  & 7 & 0.7841 & 0.7911 & 0.8025 & 0.8196 \\ \cline{1-1}  & 8 & 0.7958 & 0.8002 & 0.8178 & 0.8219 \\ \hline \end{tabular}
\end{table} 
Table 9: Analysis based on k-fold. Using cluster size = 9, and considering the Cleveland database, the proposed Taylor-BSA-DBN showed maximal accuracy of 0.934, which is 16.92%, 11.13%, and 3.96%, better than the existing methods, such as SVM, NB, and DBN, respectively. In the existing methods, the DBN offers maximum sensitivity of 0.913, but the proposed method is 3.89% better than the existing DBN. The proposed method has a maximum specificity of 0.903. The percentage of improvement of the proposed method with the existing methods, such as SVM, NB, and DBN, is 23.15%, 15.28%, and 3.10%, respectively. Considering the Hungarian database, the proposed Taylor-BSA-DBN showed maximal accuracy of 0.902, maximal sensitivity of 0.909, and maximal specificity of 0.893. Considering the Switzerland database, the proposed Taylor-BSA-DBN showed maximal accuracy of 0.840, which is 19.17%, 10.12%, and 2.38%, better than the existing methods, such as SVM, NB, and DBN, respectively. Similarly, the proposed system has a maximum sensitivity of 0.846. The percentage of improvement of the proposed system sensitivity with the existing methods, such as SVM, NB, and DBN is 19.74%, 11.35%, and 1.89%, respectively. Likewise, the proposed Taylor-BSA-DBN showed maximal specificity of 0.833. Table 11 shows the computational time of the proposed system and the existing methods, such as SVM, NB, and DBN, in which the proposed Taylor-BSA-DBN has a minimum computation time of 6.31 sec. Table 12 shows the statistical analysis of the proposed work and the existing methods based on mean and variance.