# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Evaluation Criteria  
Accuracy (Acc), sensitivity (Sens), precision (Prec), F1_score, and specificity (Spec), which are calculated from the confusion matrix, are chosen as metrics to assess the effectiveness of the model. The confusion matrix or error matrix is a useful tool for visualizing the classifier's whole performance. These datasets used in this study are multi-classified and binary, with a 2 \(\times\) 2 confusion matrix for binary and n \(\times\) n confusion matrix for multi-classification. The Acc, Sen, Pre, Spec, and F1_score of each category can be expressed as follows:  
\[Acc=\frac{TP+TN}{TP+FP+FN+TN} \tag{16}\]  
\[Prec=\frac{TP}{TP+FP} \tag{17}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Parameter Meaning** & **Parameter Value** \\ \hline \(\lambda\) & Coefficient for the L2 weight & \(1\times 10^{-4}\), \(1\times 10^{-3}\), \\  & regularization term. & \(1\times 10^{-2}\) \\ \(\beta\) & Coefficient for the sparsity & 1, 2, 3, 4, 5, 6 \\  & regularization term. & [0.02, 0.1] \\ \(\rho\) & Sparse parameter. & [0.02, 0.1] \\ PID hidden units & Number of FESSAE hidden-layer units in PID dataset & 120–40–16 \\ Maxlettle hidden units & Number of FESSAE hidden-layer units in Maxlettle dataset & 160–80–42 \\ Heart hidden units & Number of FESSAE hidden-layer units in Heart dataset & 100–60–24 \\ PD hidden units & Number of FESSAE hidden-layer units in PD dataset & 200–100–48 \\ AD hidden units & Number of FESSAE hidden-layer units in AD dataset & 240–120–60 \\ Wisconsin hidden units & Number of FESSAE hidden-layer units in Wisconsin dataset & 120–40–16 \\ Vehicle hidden units & Number of FESSAE hidden-layer units in Vehicle dataset & 120–60–32 \\ WDBC hidden units & Number of FESSAE hidden-layer units in WDBC dataset & 200–110–60 \\ lung cancer hidden units & Number of FESSAE hidden-layer units in lung cancer dataset & 424–210–106 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter information. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Instances** & **Attributes** & **Class** & **Relevant Paper** \\ \hline Statalog Heart Data Set (Heart) & 270 & 13 & 2 & Reference [35] \\ Pima Indians Diabetes Data Set (PID) & 768 & 8 & 2 & Reference [36] \\ Parkinson Speech Dataset (PD) & 1040 & 26 & 2 & Reference [37] \\ Alzheimer’s disease (AD) & 90 & 32 & 3 & Reference [38] \\ Breast Cancer Wisconsin Original (Wisconsin) & 683 & 9 & 2 & Reference [39] \\ Maxlettle Parkinson Dataset (Maxlettle) & 195 & 22 & 2 & Reference [40] \\ Stalog Vehicle Silhouettes (Vehicle) & 846 & 18 & 4 & Reference [41] \\ Breast Cancer Wisconsin Diagnostic(WDBC) & 569 & 30 & 2 & Reference [42] \\ lung cancer & 32 & 56 & 3 & Reference [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic information of datasets used in study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **Prec (\%)** & **Sens (\%)** & **Spec (\%)** & **F1\_Score** \\ \hline \multirow{3}{*}{PD} & SS & 81.35 & 81.22 & **91.73** & 80.96 & 81.41 \\  & PS & **87.69** & **87.02** & 89.04 & **86.35** & **87.83** \\  & SS & 50.00 & 52.04 & 50.00 & **75.00** & 50.87 \\  & PS & **74.44** & **75.50** & **74.44** & 74.96 & **74.96** \\  & SS & 98.53 & 97.95 & 99.15 & 97.91 & 98.54 \\ Wisconsin & PS & **99.16** & **98.77** & **99.58** & **98.74** & **99.17** \\  & SS & 86.68 & 86.49 & 86.68 & 95.57 & 86.59 \\ Vehicle & PS & **90.83** & **90.79** & **90.82** & **96.95** & **90.80** \\  & SS & 96.70 & 96.84 & 96.71 & 96.67 & 96.76 \\ WDBC & PS & **98.35** & **98.67** & **98.12** & **98.57** & **98.38** \\ lung & SS & 66.67 & 50.00 & 38.89 & 75.00 & 43.75 \\ cancer & PS & **91.74** & **87.77** & **91.11** & **93.67** & **89.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._