# Classification of Valvular Regurgitation Using Echocardiography
## 4 Experimental Results
### Classification Comparison with Existing Methodologies
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fold**} & \multirow{2}{*}{
\begin{tabular}{c} **Performance** \\ **Metrics** \\ \end{tabular} } & \multicolumn{2}{c}{**With Segmentation**} & \multicolumn{6}{c}{**Without Segmentation**} \\  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{2-folds} & Accuracy & 66.66 & 69.81 & 87.73 & 61.32 & 61.32 & 94.34 & 98.76 & 85.84 & 86.79 & 96.26 \\  & Precision & 64.57 & 69.13 & 86.76 & 61.27 & 59.58 & 94.30 & 98.30 & 84.74 & 87.25 & 96.25 \\  & Recall & 67.58 & 68.24 & 87.55 & 62.79 & 61.01 & 94.07 & 97.28 & 85.89 & 87.05 & 96.22 \\  & F1-score & 65.06 & 68.25 & 86.51 & 59.01 & 59.30 & 94.14 & 97.75 & 84.96 & 86.70 & 96.19 \\ \hline \multirow{4}{*}{3-folds} & Accuracy & 85.91 & 50.70 & 76.05 & 67.60 & 76.05 & 93.05 & 91.54 & 90.27 & 94.36 & 94.36 \\  & Precision & 85.23 & 50.49 & 75.51 & 67.86 & 73.80 & 92.54 & 91.41 & 90.34 & 94.08 & 94.17 \\  & Recall & 85.30 & 50.54 & 75.84 & 67.62 & 73.56 & 92.22 & 91.17 & 90.44 & 94.08 & 93.70 \\  & F1-score & 84.93 & 50.38 & 75.45 & 67.59 & 72.92 & 92.35 & 91.26 & 90.29 & 94.08 & 93.88 \\ \hline \multirow{4}{*}{5-folds} & Accuracy & 71.42 & 73.17 & 85.71 & 50.00 & 71.42 & 100 & 90.47 & 100 & 90.69 & 100 \\  & Precision & 70.36 & 71.89 & 84.86 & 53.11 & 72.11 & 100 & 89.91 & 100 & 89.72 & 100 \\ \cline{1-1}  & Recall & 70.67 & 72.13 & 84.24 & 50.59 & 73.79 & 100 & 90.69 & 100 & 88.54 & 100 \\ \cline{1-1}  & F1-score & 70.43 & 71.95 & 84.34 & 58.17 & 72.07 & 100 & 90.22 & 100 & 90.22 & 100 \\ \hline \multirow{4}{*}{8-folds} & Accuracy & 57.69 & 61.53 & 73.07 & 84.61 & 84.61 & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & Precision & 66.02 & 62.32 & 72.68 & 83.93 & 83.33 & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & Recall & 58.88 & 62.03 & 73.33 & 83.93 & 81.42 & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & F1-score & 57.32 & 61.56 & 72.84 & 83.80 & 82.18 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics for 10%, 20%, 30%, 40% and 50% testing data using Redundant frame extraction for with and without segmentation for two, three, five, and eight folds. output obtained from Tables 6 and 7 for the proposed methodology, in which five folds show promising results compared to other folds. The result is provided in Table 9.  
The following summary has been observed after implementing the SVM, PCA-SVM, and the proposed approach. 1. From Table 9, it can be observed that our method provides a better result in most cases compared to PCA-SVM and SVM without segmentation and better accuracy to both the methods in the case when using segmentation. 2. In the majority of cases, using 10% testing data provides more promising results than when using 20% or more data. This is mainly due to the data size, which is small in our case. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Author** & **Methodologies Used** & **Types of Classification** & **Type of Images** & **Accuracy (\%)** \\ \hline [PERSON] [12] (2017) & JICA, PCA, SVM & Types of regurgitation & Static & 82 \\ \hline [PERSON] [11] (2010) & \begin{tabular}{c} Affine transform, \\ Histogram, Pyramid \\ matching, SVM \\ \end{tabular} & \begin{tabular}{c} Normal or abnormal \\ (hypothesis) \\ \end{tabular} & Static & 90.5 \\ \hline Proposed & \
\begin{tabular}{c} Binarization, Levelset \\ method, Haralick and \\ GLCM, Random Forest \\ \end{tabular} & Types of regurgitation & Videographic &
\begin{tabular}{c} 95.33 (Highest obtained \\ accuracy in case of \\ reference frame \\ without segmentation) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison table for all the methodologies in the classification of heart abnormalities.