# Classification of Valvular Regurgitation Using Echocardiography
## 4 Experimental Results
### Output of Proposed Methodology
#### 4.3.3 Classification Output  
After feature extraction, RF is used to classify the images into three types of regurgitation, namely AR, MR, and TR. Here, two types of classification are carried out which is reference frame classification and redundant frame classification. For each type of classification, two approaches have been used, the segmentation approach and the without segmentation approach. It is crucial to determine whether or not segmentation is an essential factor in classifying regurgitation in the heart. It also gives us a clear idea of validation and analysis of images in the future. The classification was assessed using four measures based on the confusion matrix. These are Accuracy, Precision, Recall, and F1-score [45]. Classification is implemented using k-fold cross-validation for statistical purposes since data used are not massive. Figure 5: The first row shows the input image, second row the preprocessed images and third row the segmented images for AR, MR and TR respectively. 1. _Accuracy._ It is the measure of correctly classified images as a percentage. It can be calculated using Equation (7). \[Accuracy=\frac{TP+TN}{TP+FP+TN+FP}\] (7)
2. _Precision_: It is the fraction of True Positives (_TP_) and False Positives (_FP_). Precision can be calculated using Equation (8). \[Precision=\frac{TP}{TP+FP}\] (8)
3. _Recall_: It represents the fraction of True Positives (_TP_) and False Negatives (_FN_). It can be calculated using Equation (9). \[Recall=\frac{TP}{TP+FN}\] (9)
4. _F1-score_: It is a harmonic mean of precision and recall. It is given by Equation (10). \[F1\text{-}Score=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (10) The following observations were made based on the output obtained from Tables 2-7. 1. Using reference frame extraction, two-fold cross-validation highest accuracy obtained is 85.29% and 91.17% for with segmentation and without segmentation, respectively, and three-, five-, and eight-fold cross-validation highest accuracy is 100% in both cases. 2. Using redundant frame extraction, the best accuracy for two-fold is 87.73% and 98.76%, three-fold is 85.91% and 94.36%, five-fold is 85.71% and 100%, and eight-fold is 84.61% and 100% for with segmentation and without segmentation, respectively. 3. The overall accuracy for two-fold is 76.64% and 83.52%, three-fold is 88.69% and 86.95%, five-fold is 89.22% and 96.46%, and eight-fold is 77.50% and 95% for with segmentation and without segmentation, respectively, in the case of the reference frame, and two-fold is 69.36% and 92.39%, three-fold is 71.26% and 92.71%, five-fold is 63.14% and 96.23%, and eight-fold is 72.30% and 100% for with segmentation and without segmentation respectively in the case of the redundant frame. The overall accuracy of two-, three-, five-, and eight-fold for with and without segmentation for reference frame is 83.56% and 90.48%, respectively. The overall accuracy of two-, three-, five-, and eight-fold for with and without segmentation for reference frame is 69.01% and 95.33%, respectively. 4. Using five folds provided the best result when testing data are divided into 10%, 20%, 30%, 40%and 50%. 5. Based on the output obtained on the accuracy, precision, recall, and F1-score, it can be seen that in most cases, without segmentation gives a better result compared to with segmentation approach. The result that reflects this can be visualized using eight-fold cross-validation, a smaller data distribution using a reference frame. 6. It can be concluded that without using segmentation can also be applied to classify regurgitation in the heart. This might not be the case for all types of diagnosis. It might not be valid for cases of data having ground truth segmentation as well. This can be true for fully unsupervised segmentation techniques and not for supervised or semisupervised segmentation. Using the redundant frame approach provides better accuracy in the case of without segmentation. \begin{table}
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**No.