# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Evaluation Criteria
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **Prec (\%)** & **Sens (\%)** & **Spec (\%)** &
\begin{tabular}{c} **F1\_Score** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{2}{*}{PID} & SS & 77.44 & 77.30 & **79.15** & 75.77 & 77.81 \\  & PS & **83.80** & **88.60** & 79.14 & **88.48** & **83.03** \\ \multirow{2}{*}{Maxlettle} & SS & 92.61 & 97.50 & 87.56 & 97.78 & 91.89 \\  & PS & **96.78** & **100** & **93.33** & **100** & **96.32** \\ \multirow{2}{*}{Heart} & SS & 91.25 & 91.64 & 90.83 & 91.67 & 91.16 \\  & PS & **97.08** & **97.59** & **96.67** & **97.50** & **97.07** \\ \multirow{2}{*}{PD} & SS & 75.00 & 73.26 & 79.33 & 70.67 & 76.07 \\  & PS & **81.49** & **80.92** & **82.69** & **80.29** & **81.69** \\ \multirow{2}{*}{AD} & SS & 58.89 & 60.82 & 58.89 & 79.44 & 59.78 \\  & PS & **62.22** & **64.08** & **62.22** & **81.11** & **63.04** \\ \multirow{2}{*}{Wisconsin} & SS & 99.16 & **98.38** & 100 & **98.32** & 99.18 \\  & PS & **99.16** & **98.38** & 100 & **98.32** & 99.18 \\ \multirow{2}{*}{Vehicle} & SS & **99.58** & **99.17** & **100** & 99.16 & **99.58** \\ \multirow{2}{*}{Vehicle} & SS & 80.77 & 82.34 & 80.78 & 93.57 & 81.54 \\  & PS & **84.53** & **85.57** & **84.54** & **94.82** & **85.05** \\ \multirow{2}{*}{WDBC} & SS & 97.41 & 97.79 & 97.18 & 97.62 & 97.42 \\  & PS & **99.76** & **99.53** & **100** & **99.53** & **99.76** \\ \multirow{2}{*}{lung} & SS & 50.00 & 50.00 & 38.89 & 75.00 & 43.75 \\ \multirow{2}{*}{lung} & SS & **87.62** & **87.78** & **91.11** & **93.67** & **89.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of sample pairs and single samples after FESSAE on RF. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **Prec (\%)** & **Sens (\%)** & **Spec (\%)** & **F1\_Score** \\ \hline \multirow{2}{*}{WDBC} & SS & 98.59 & **99.53** & 97.64 & 99.52 & 98.57 \\  & PS & **98.59** & 99.52 & **97.65** & **99.52** & **98.57** \\ \multirow{2}{*}{lung} & SS & 83.33 & 83.33 & 88.89 & 91.67 & 86.02 \\ \multirow{2}{*}{cancer} & PS & **94.28** & **93.33** & **96.67** & **96.67** & **94.92** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of sample pair and single samples after LDA on RF. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **Prec (\%)** & **Sens (\%)** & **Spec (\%)** & **F1\_Score** \\ \hline \multirow{2}{*}{PID} & SS & 77.44 & 77.30 & 79.15 & 75.77 & 77.81 \\  & PS & **83.80** & **88.60** & 79.14 & **88.48** & **83.03** \\ \multirow{2}{*}{Maxlettle} & SS & 99.00 & 100 & **98.00** & 100 & **98.95** \\  & PS & 98.89 & 100 & 97.78 & 100 & 98.82 \\ \multirow{2}{*}{Heart} & SS & 91.25 & 91.64 & 90.83 & 91.67 & 91.16 \\  & PS & **97.08** & **97.59** & **96.67** & **97.50** & **97.07** \\ \multirow{2}{*}{PD} & SS & 75.00 & 73.26 & 79.33 & 70.67 & 76.07 \\  & PS & **81.49** & **80.92** & **82.69** & **80.29** & **81.69** \\ \multirow{2}{*}{AD} & SS & 71.11 & 74.96 & 71.11 & 85.56 & 72.95 \\ \multirow{2}{*}{Wisconsin} & PS & **75.56** & **78.70** & **75.56** & **87.78** & **77.07** \\  & SS & 99.16 & 98.38 & 100 & **98.32** & 99.18 \\ \multirow{2}{*}{Wisconsin} & PS & **99.58** & **99.18** & **100** & 99.16 & **99.58** \\ \multirow{2}{*}{Vehicle} & SS & 80.77 & 82.34 & 80.78 & 93.57 & 81.54 \\  & PS & **84.53** & **85.57** & **84.54** & **94.82** & **85.05** \\ \multirow{2}{*}{WDBC} & SS & 97.41 & 97.79 & 97.18 & 97.62 & 97.42 \\ \multirow{2}{*}{JD} & PS & **99.76** & **99.53** & **100** & **99.53** & **99.76** \\ \multirow{2}{*}{lung} & SS & 50.00 & 50.00 & 38.89 & 75.00 & 43.75 \\ \multirow{2}{*}{cancer} & PS & **87.62** & **87.78** & **91.11** & **93.67** & **89.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of sample pairs and single samples after FESSAE on SVM. As seen from Tables 7 and 8, most of the sample-pair-based methods are better than the single-sample-based results in these databases with the LDA on the SVM and RF classifiers.