# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Evaluation Criteria
#### 4.3.2 Effectiveness Analysis of DFLM  
Verification of the performance of the different steps in DFLM is conducted by ablation experiments. First, the sample pairs are input to the classification model as original features (OFs) input. Second, the samples are processed with the FESSAE and the OFs converted into deep features (DFs). Third, OFs and DFs are combined to construct new features (CF). The CF is processed by the L1_wLPPD to become the reduced features (CF and DFLM). Different feature sets are used with the same classifier and dataset. A comparison of the results is shown in Table 11.  
Table 11 shows that in some cases the CF and DFLM has better results. This result indicates that the proposed diamond-like feature learning mechanism (DFLM) is effective. The accuracy of DFs also performs well. The results show that FESSAE is effective, and it can learn as many high-quality features as possible and expand features well. The fact that \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **Prec (\%)** & **Sens (\%)** & **Spec (\%)** & **F1, Score** \\ \hline \multirow{4}{*}{PID} & OFs & 79.29 & 79.30 & 79.48 & 79.11 & 79.32 \\  & DFs & 83.80 & **88.60** & 79.14 & **88.48** & 83.03 \\  & CF & 82.31 & 86.97 & 77.66 & 86.97 & 81.38 \\  & CF and DFLM & **84.54** & 88.50 & **80.99** & 88.11 & **84.18** \\ \hline \multirow{4}{*}{Heart} & OFs & 91.67 & 94.19 & 89.17 & 94.17 & 91.54 \\  & DFs & **97.08** & 77.59 & 96.67 & 97.50 & **97.07** \\  & CF & 96.25 & 97.51 & 95.00 & 97.50 & 96.13 \\  & CF and DFLM & 96.67 & **97.59** & **95.83** & **97.50** & 96.65 \\ \hline \multirow{4}{*}{PD} & OFs & 68.94 & _69.84_ & 67.31 & 70.58 & 68.29 \\  & DFs & **81.49** & **80.92** & **82.69** & **80.29** & **81.69** \\  & CF & 78.73 & 81.36 & 75.96 & 81.49 & 77.53 \\  & CF and DFLM & 79.93 & 80.05 & 79.81 & 80.05 & 79.60 \\ \hline \multirow{4}{*}{AD} & OFs & 58.89 & 65.78 & 58.89 & 79.44 & 62.07 \\  & DFs & **75.56** & **78.70** & **75.56** & **87.78** & **77.07** \\  & CF & 68.89 & 69.97 & 68.89 & 84.44 & 69.41 \\  & CF and DFLM & 72.22 & 74.55 & 72.22 & 86.11 & 73.36 \\ \hline \multirow{4}{*}{Wisconsin} & OFs & 97.70 & 98.31 & 97.07 & 98.32 & 97.67 \\  & DFs & **99.58** & 99.18 & 100 & 99.16 & 99.58 \\  & CF & 98.95 & 98.76 & 99.16 & 98.74 & 98.95 \\  & CF and DFLM & 99.37 & **98.77** & **100** & **98.74** & **99.58** \\ \hline \multirow{4}{*}{Vehicle} & OFs & 84.65 & 84.76 & 84.65 & 94.90 & 84.71 \\  & DFs & 84.53 & 85.56 & 84.54 & 97.82 & 85.05 \\  & CF & 85.54 & 86.37 & 85.55 & 95.18 & 85.95 \\  & CF and DFLM & **87.80** & **88.73** & **87.82** & **95.93** & **88.27** \\ \hline \multirow{4}{*}{WDBC} & OFs & 99.06 & 100 & 98.11 & 100 & 99.04 \\  & DFs & **99.76** & 99.53 & **100** & 99.53 & **99.76** \\  & CF & 98.82 & 99.09 & 98.58 & 99.05 & 98.82 \\  & CF and DFLM & 99.53 & **100** & 99.53 & **100** & 99.53 \\ \hline \multirow{4}{*}{Maxlettle} & OFs & 81.24 & 84.37 & 79.33 & 83.33 & 81.02 \\  & DFs & **98.89** & **100** & **97.78** & **100** & **98.82** \\ \cline{1-1}  & CF & 97.84 & 100 & 95.56 & 100 & 97.65 \\ \cline{1-1}  & CF and DFLM & 97.89 & 100 & 95.56 & 100 & 97.50 \\ \hline \multirow{4}{*}{lung cancer} & OFs & 91.75 & 87.78 & 91.11 & 93.67 & 89.33 \\ \cline{1-1}  & DFs & **97.14** & **96.67** & **98.33** & **98.33** & **97.46** \\ \cline{1-1}  & CF & 87.62 & 87.78 & 91.11 & 93.67 & 89.40 \\ \cline{1-1}  & CF and DFLM & 95.24 & 91.67 & 96.67 & 95.83 & 93.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Result of principal stages of the proposed method. the CF and DFLM performs better than the CF means that the proposed feature reduction mechanism is effective. The accuracy of the CF does not improve as much as that of the OFs. We hypothesize that the reason is that the simple combination of the OFs and DFs leads to high redundancy. Therefore, it needs to consider feature reduction. Different datasets have different results, due to characteristics of data and disease.