# Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
## 4 Results and Discussion
### Comparative Analysis
#### 4.5.2 Analysis with Cluster Size = 9  
The analysis of methods considering cluster size = 9, using Cleveland, Hungarian, and Switzerland databases are specified below:  
Analysis Considering Cleveland Database  
Table 5 depicts the analysis of methods using the Cleveland database, considering training data with accuracy, sensitivity, and specificity parameters. The maximum accuracy, sensitivity, and specificity are considered as the best performances. Here, the proposed system offers better performance than the existing methods, such as SVM, NB, and DBN, respectively. Analysis Considering Hungarian Database  
Table 6 shows the analysis of methods using the Hungarian database, considering training data with accuracy, sensitivity, and specificity parameters. The proposed system offers the best performance when considering 90% of training data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**SVM**} & \multicolumn{1}{c}{**NB**} & \multicolumn{1}{c}{**DBN**} & \multicolumn{1}{c}{**Proposed Taylor-BSA–DBN**} \\ \hline
**Training Percentage** & & & & \\ \hline  & & Accuracy & & \\
50 & 0.7619 & 0.7710 & 0.7895 & 0.8644 \\
60 & 0.7009 & 0.7374 & 0.7800 & 0.8557 \\
70 & 0.7073 & 0.7568 & 0.7895 & 0.8904 \\
80 & 0.7551 & 0.7647 & 0.7778 & 0.8400 \\
90 & 0.6774 & 0.7037 & 0.7143 & 0.8462 \\ \hline  & & Sensitivity & & \\
50 & 0.7656 & 0.7742 & 0.7818 & 0.8710 \\
60 & 0.6981 & 0.7292 & 0.7843 & 0.8627 \\
70 & 0.7073 & 0.7500 & 0.7948 & 0.8974 \\
80 & 0.7500 & 0.7692 & 0.7857 & 0.8462 \\
90 & 0.6875 & 0.6923 & 0.7143 & 0.8571 \\ \hline  & & Specificity & & \\
50 & 0.7581 & 0.7667 & 0.7966 & 0.8571 \\
60 & 0.7037 & 0.7451 & 0.7755 & 0.8478 \\
70 & 0.7073 & 0.7632 & 0.7838 & 0.8823 \\
80 & 0.7600 & 0.7600 & 0.7692 & 0.8333 \\
90 & 0.6667 & 0.7143 & 0.7143 & 0.8333 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of methods with cluster size = 5 using the Switzerland database. Table 7 depicts the analysis of methods using the Switzerland database considering training data with accuracy, sensitivity, and specificity parameters. The better performance of the proposed system with values is 0.7778, 0.7857, and 0.7692, for the performance metrics, such as accuracy, sensitivity, and specificity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **SVM** & **NB** & **DBN** & **Proposed Taylor-BSA–DBN** \\ \hline
**Training Percentage** & & & & \\ \hline \multicolumn{4}{c}{Accuracy} \\
50 & 0.7500 & 0.7957 & 0.8515 & 0.9200 \\
60 & 0.7513 & 0.7870 & 0.8075 & 0.8907 \\
70 & 0.7777 & 0.8273 & 0.8500 & 0.9118 \\
80 & 0.7755 & 0.8298 & 0.8974 & 0.9341 \\
90 & 0.7674 & 0.8000 & 0.8298 & 0.8696 \\ \hline \multicolumn{4}{c}{Sensitivity} \\
50 & 0.7907 & 0.8389 & 0.8844 & 0.9388 \\
60 & 0.8017 & 0.8218 & 0.8487 & 0.9160 \\
70 & 0.8242 & 0.8652 & 0.8732 & 0.9326 \\
80 & 0.8226 & 0.8667 & 0.9130 & 0.9500 \\
90 & 0.8077 & 0.8438 & 0.8667 & 0.9000 \\ \hline \multicolumn{4}{c}{Specificity} \\
50 & 0.6897 & 0.7209 & 0.7927 & 0.8846 \\
60 & 0.6667 & 0.7353 & 0.7353 & 0.8438 \\
70 & 0.6981 & 0.7600 & 0.8163 & 0.8723 \\
80 & 0.6944 & 0.7647 & 0.8750 & 0.9032 \\
90 & 0.7059 & 0.7222 & 0.7647 & 0.8125 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of methods with cluster size = 9 using the Hungarian database.