# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Parameter Analysis  
#### 4.5.1 Analysis of FESSAE Parameters  
The FESSAE model is a critical part of the SP_DFSaeLA. Therefore, it is critical to assess the impact of different FESSAE parameter settings on overall performance. First, the effect of the FESSAE's sparsity parameter is discussed. The sparsity parameter, ranging from 0.02 to 0.1, is chosen based on previous research. As seen from Figure 9, different sparse parameters give different results. The sparse parameter had an apparent impact for the FESSAE algorithm's accuracy. The optimal sparse parameter is different for different datasets, and there is no fixed selection criterion. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Performance**} & **Literature** & **Literature** & **Literature** & **Proposed Method** \\  & & [54] & [55] & [56] & [57] & **Method** \\  & & **(S-Fold)** & **(S-Fold)** & **(S-Fold)** & **(S-Fold)** & **(Holdout)** \\ \hline \multirow{4}{*}{PID} & Acc (\%) & 97.38 & 81.13 & 80.19 & **84.54** \\  & Sens (\%) & **87.50** & 83.02 & 79.25 & 80.99 \\  & Spec (\%) & 71.25 & 79.25 & 81.13 & **88.11** \\  & Prec (\%) & 75.27 & 80.00 & 80.77 & **88.50** \\  & F1\_score (\%) & 80.92 & 81.48 & 80.00 & **84.18** \\ \hline \multirow{4}{*}{WDBC} & Acc (\%) & 96.85 & 98.82 & 95.29 & **99.76** \\  & Sens (\%) & 96.83 & **100** & 92.86 & 99.53 \\ \cline{1-1}  & Spec (\%) & 96.88 & 97.67 & 97.67 & **100** \\ \cline{1-1}  & Prec (\%) & 96.83 & 97.67 & 97.50 & **100** \\ \cline{1-1}  & F1\_score (\%) & 96.83 & 98.82 & 95.12 & **99.76** \\ \hline \multirow{4}{*}{Wisconsin} & Acc (\%) & 94.41 & 97.89 & 95.79 & **99.37** \\  & Sens (\%) & 94.37 & **100** & 95.74 & 100 \\ \cline{1-1}  & Spec (\%) & 94.44 & 95.74 & 95.83 & **98.74** \\ \cline{1-1}  & Prec (\%) & 94.37 & 96.00 & 95.74 & **98.77** \\ \cline{1-1}  & F1\_score (\%) & 94.37 & 97.86 & 96.00 & **99.38** \\ \hline \hline \multirow{4}{*}{Heart} & Acc (\%) & 83.33 & 95.83 & 85.42 & **96.67** \\ \cline{1-1}  & Sens (\%) & 91.67 & **100** & 83.33 & 95.83 \\ \cline{1-1}  & Spec (\%) & 75.00 & 91.67 & 87.50 & **97.50** \\ \cline{1-1}  & Prec (\%) & 78.57 & 92.31 & 86.96 & **97.59** \\ \cline{1-1}  & F1\_score (\%) & 84.62 & 96.00 & 85.00 & **96.65** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison of proposed method with state-of-the-art chronic disease detection algorithms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Performance**} & **Literature** & **Literature** & **Literature** & **Proposed** & **Proposed** \\  & & [54] & [55] & [56] & [57] & **Method** & **Method** \\  & & **(S-Fold)** & **(S-Fold)** & **(Holdout)** & **(S-Fold)** & **(S-Fold)** & **(Holdout)** \\ \hline \multirow{4}{*}{WDBC} & Acc (\%) & 97.3 & 95.6 & 97.65 & 95.61 & **99.76** & 98.82 \\  & Sens (\%) & 83.0 & 95.1 & 95.35 & - & 99.53 & **100** \\  & Spec (\%) & 95.7 & - & - & - & **100** & **98.82** \\  & Prec (\%) & 87.8 & 96.0 & 100 & - & **100** & 97.67 \\  & F1\_score (\%) & - & 95.5 & 97.62 & - & **99.76** & 98.82 \\ \hline \multirow{4}{*}{Wisconsin} & Acc (\%) & 98.1 & 97.1 & 96.84 & 96.79 & **99.37** & 98.95 \\  & Sens (\%) & 53.3 & 97.1 & 97.87 & - & 100 & **100** \\ \cline{1-1}  & Spec (\%) & **99.5** & - & - & - & 98.74 & 97.87 \\ \cline{1-1}  & Prec (\%) & 86.7 & 97.2 & 95.83 & - & **98.77** & 97.96 \\ \cline{1-1}  & F1\_score (\%) & - & 97.1 & 96.84 & - & **99.38** & 98.97 \\ \hline \multirow{4}{*}{Heart} & Acc (\%) & 73.9 & 86.7 & 85.42 & 82.96 & **96.67** & 95.83 \\ \cline{1-1}  & Sens (\%) & 74.8 & 81.7 & 75.00 & - & 95.83 & **100** \\ \cline{1-1}  & Spec (\%) & 73.0 & - & - & - & **97.50** & 91.67 \\ \cline{1-1}  & Prec (\%) & 69.7 & 90.8 & 94.74 & - & **97.59** & 92.31 \\ \cline{1-1}  & F1\_score (\%) & - & 86.0 & 83.72 & - & **96.65** & 96.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: _Cont._The datasets used have different numbers of samples, feature dimensions, and numbers of categories, and the optimal sparse parameter may be related to these factors.