## 3 Results
In Table 6, accuracy of 95% with CI of 88.72% to 98.36% using transvers scan is observed. Similarly, Table 7 showed highest accuracy of (96.6% with CI of 94.90% to 97.86%) using BUS images collected at BHE. Furthermore, high values of specificity (97.70% with CI of 95.81% to 98.89%) and NPV (97.25% with CI of 95.34% to 98.40%) are observed. As shown in Tables 5-7, the ensemble classifier offered superior results as compared to others. The prime reason is that ensemble algorithm uses multiple learning technologies to attain an enhanced discriminative power than any other single classifier [42]. The ensemble decision tree classifiers with RUSBoost have a good result due to limited generalization error in the large number of growing trees in the classifiers. RUSBoost employs a mix of random under sampling and a boosting method to improve performance [43]. Ensemble classifiers are better; however, it consumes more time as compared to decision tree and KNN. It should be noted that the results also depend upon the nature of the domain, dataset size, and selection of features. Certain classifiers perform well on some type of applications or data better than the others. The overall classification accuracy of employed classifiers, that is, ensemble, KNN, and decision tree have been observed to be 97%, 94%, and 88% for longitudinal scan and 95%, 93%, and 85% for transverse scan of the OASBUD dataset, respectively as shown in Figure 8. Similarly, the observed classification accuracy of BUS images collected at BHE using ensemble, decision tree, and KNN are \begin{table}
\begin{tabular}{c c c} \hline \hline Ensemble (Preset: RUSBoosted trees, Ensemble method: RUSBoost, learner type: Decision tree, number of learners: 30, maximum number of splits: 20, learning rate: 0.1) \\ \hline
**Statistic** & **Value** & **95\% of Confidence Interval** \\ \hline Accuracy & 96.60\% & 94.90\% to 97.86\% \\ Sensitivity & 94.34\% & 90.32\% to 97.04\% \\ Specificity & 97.70\% & 95.81\% to 98.89\% \\ PPV & 95.24\% & 91.55\% to 97.36\% \\ NPV & 97.25\% & 95.34\% to 98.40\% \\ \hline \hline \end{tabular} Decision tree (Preset: Complex tree, split criterion: Gini’s diversity index, maximum number of splits: 100)  
\begin{tabular}{c c c} \hline \hline Accuracy & 95.83\% & 93.99\% to 97.23\% \\ Sensitivity & 92.96\% & 88.65\% to 96.01\% \\ Specificity & 97.24\% & 95.22\% to 98.56\% \\ PPV & 94.29\% & 90.42\% to 96.65\% \\ NPV & 96.57\% & 94.53\% to 97.87\% \\ \hline \hline \end{tabular} KNN (Preset: Weighted KNN, number of neighbors: 10, distance weight: Squared inverse, distance metric: Euclidean)  
\begin{tabular}{c c c} \hline \hline Accuracy & 95.36\% & 93.45\% to 96.85\% \\ Sensitivity & 92.86\% & 88.49\% to 95.95\% \\ Specificity & 96.57\% & 94.40\% to 98.07\% \\ PPV & 92.86\% & 88.76\% to 95.54\% \\ NPV & 96.57\% & 94.53\% to 97.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance based on hybrid feature vector using breast ultrasound (BUS) images collected at the Baheya Hospital Egypt (BHE). 96.6%, 95.83%, and 95.36%, respectively. The results prove that the ensemble classifier decision tree model through RUSBoost offers the best accuracy among all the employed classifiers.