# Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
## 4 Results and Discussion
### Comparative Discussion  
Table 10 portrays the analysis of methods using accuracy, sensitivity, and specificity parameter with varying training data. The analysis is done with Cleveland, Switzerland, and Hungarian databases. Using cluster size = 5, and considering the Cleveland database, the proposed Taylor-BSA-DBN showed maximal accuracy of 0.871, which is 13.43%, 12.17%, and 11.14%, better than the existing methods, such as SVM, NB, and DBN, respectively. In the existing methods, the DBN offers maximum sensitivity of 0.771, but the proposed method is 12.29% better than the existing DBN. The proposed method has a maximum specificity of 0.862. The percentage of improvement of the proposed method with the existing methods, such as SVM, NB, and DBN, is 12.99%, 12.06%, and 9.40%, respectively. Considering the Hungarian database, the proposed Taylor-BSA-DBN \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **SVM** & **NB** & **DBN** & **Proposed Taylor-BSA–DBN** \\ \hline
**Training Percentage** & & & Accuracy & \\ \hline  & & & & \\
50 & 0.7460 & 0.7479 & 0.8017 & 0.8644 \\
60 & 0.7170 & 0.7624 & 0.7684 & 0.8947 \\
70 & 0.7368 & 0.7500 & 0.7662 & 0.8904 \\
80 & 0.6786 & 0.7551 & 0.8200 & 0.8400 \\
90 & 0.7333 & 0.7600 & 0.7679 & 0.7778 \\ \hline  & & & & \\
50 & 0.7414 & 0.7500 & 0.8065 & 0.8710 \\
60 & 0.7170 & 0.7609 & 0.7647 & 0.9020 \\
70 & 0.7297 & 0.7561 & 0.7692 & 0.8974 \\
80 & 0.6786 & 0.7500 & 0.8300 & 0.8462 \\
90 & 0.7300 & 0.7500 & 0.7667 & 0.7857 \\ \hline  & & & & \\
50 & 0.7419 & 0.7541 & 0.7966 & 0.8571 \\
60 & 0.7170 & 0.7600 & 0.7755 & 0.8864 \\
70 & 0.7436 & 0.7436 & 0.7632 & 0.8824 \\
80 & 0.6786 & 0.7600 & 0.8200 & 0.8333 \\
90 & 0.7143 & 0.7300 & 0.7556 & 0.7692 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of methods with cluster size = 9 using the Switzerland database. showed maximal accuracy of 0.913, maximal sensitivity of 0.933, and maximal specificity of 0.875. Considering the Switzerland database, the proposed Taylor-BSA-DBN showed maximal accuracy of 0.846, which is 19.98%, 16.78%, and 15.60% better than the existing methods, such as SVM, NB, and DBN, respectively. Similarly, the proposed system has a maximum sensitivity of 0.857. The percentage of improvement of the proposed system sensitivity, with the existing methods, such as SVM, NB, and DBN is 19.72%, 19.25%, and 16.69%, respectively. Likewise, the proposed Taylor-BSA-DBN showed maximal specificity of 0.833. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **SVM** & **NB** & **DBN** & **Proposed Taylor-BSA–DBN** \\ \hline
**FPR** & & & **TPR** & \\ \hline  & & & Cleveland & \\
1 & 0 & 0 & 0 & 0 \\
2 & 0.7913 & 0.7949 & 0.8429 & 0.8761 \\
3 & 0.7961 & 0.8330 & 0.8523 & 0.8798 \\
4 & 0.8462 & 0.8753 & 0.9149 & 0.9284 \\
5 & 0.8857 & 0.9119 & 0.9535 & 0.9684 \\
6 & 0.9153 & 0.9569 & 0.9788 & 0.9847 \\
7 & 0.9710 & 0.9783 & 0.9895 & 0.9975 \\
8 & 0.9952 & 0.9989 & 1 & 1 \\
9 & 1 & 1 & 1 & 1 \\
10 & 1 & 1 & 1 & 1 \\ \hline  & & & Hungarian & \\
1 & 0 & 0 & 0 & 0 \\
2 & 0.8233 & 0.8410 & 0.8553 & 0.8941 \\
3 & 0.8286 & 0.8647 & 0.8734 & 0.8953 \\
4 & 0.9030 & 0.9130 & 0.9233 & 0.9348 \\
5 & 0.9246 & 0.9417 & 0.9596 & 0.9789 \\
6 & 0.9521 & 0.9697 & 0.9803 & 0.9999 \\
7 & 0.9793 & 0.9800 & 0.9946 & 1 \\
8 & 0.9981 & 0.9985 & 1 & 1 \\
9 & 1 & 1 & 1 & 1 \\
10 & 1 & 1 & 1 & 1 \\ \hline  & & & Switzerland & \\
1 & 0 & 0 & 0 & 0 \\
2 & 0.7593 & 0.7682 & 0.8024 & 0.8258 \\
3 & 0.7620 & 0.7923 & 0.8399 & 0.8682 \\
4 & 0.8452 & 0.8735 & 0.8781 & 0.9101 \\
5 & 0.8725 & 0.9184 & 0.9194 & 0.9564 \\
6 & 0.9105 & 0.9443 & 0.9569 & 0.9794 \\
7 & 0.9701 & 0.9722 & 0.9865 & 0.9924 \\
8 & 0.9946 & 0.9953 & 0.9994 & 1 \\
9 & 1 & 1 & 1 & 1 \\
10 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Analysis based on ROC.