# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 4 Experimental Results
### Evaluation Metrics of the Algorithms and Classifiers That Were Applied in the Proposed Model
#### 4.2.3 Mean Accuracy and Confidence Intervals of Classification Algorithms  
This section presents the mean accuracy and the associated 95% confidence intervals for various classification models. Mean accuracy indicates the overall effectiveness of each model in correctly classifying the data. The 95% confidence intervals show the range within which the true accuracy. This takes into account the variability in the model's performance across different data subsets, providing a clearer understanding of the model's reliability and stability.  
Table 5 presents the analysis of classification algorithms for the DermIS dataset using EfficientNetB0-PCA, as shown by the mean accuracy and 95% confidence intervals, demonstrates that all four models--Decision Tree (DT), Random Forest (RF), Neural Network (NN), and SVM--perform at a high level with comparable accuracy. The mean accuracies are 90.40% for DT, 90.92% for RF, 90.93% for NN, and 91.00% for SVM. The overlapping confidence intervals indicate no significant differences between their performances, suggesting that any of these algorithms can be effectively used for this classification task, providing flexibility in model selection based on other considerations such as computational resources or ease of implementation.  
Table 6 shows the analysis of classification algorithms for the DermIS dataset using EfficientNetB0-PCA, demonstrating high performance across all models: DT (91.38%), RF (91.88%), NN (92.60%), and SVM (93.00%). The overlapping 95% confidence intervals indicate no significant performance differences, suggesting that any of these models can be effectively used for this task.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Algorithms** & **Mean Accuracy (\%)** & **95\% Confidence Intervals (\%)** \\ \hline EfficientNetB0-PCA DT & 91.38 & (89.29, 93.46) \\ EfficientNetB0-PCA RF & 91.88 & (90.34, 93.41) \\ EfficientNetB0-PCA NN & 92.60 & (91.17, 94.03) \\ EfficientNetB0-PCA SVM & 93.00 & (91.72, 94.28) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean accuracy and confidence intervals of classification algorithms for DemIS dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Algorithms** & **T-Statistic** & \(p\)**-Value** \\ \hline EfficientNetB0-PCA (DT vs. NN) & \(-1.0966\) & \(0.2873\) \\ EfficientNetB0-PCA (DT vs. RF) & \(-0.4370\) & \(0.6673\) \\ EfficientNetB0-PCA (DT vs. SVM) & \(-1.5033\) & \(0.1501\) \\ EfficientNetB0-PCA (NN vs. R) & \(0.7823\) & \(0.4442\) \\ EfficientNetB0-PCA (NN vs. SVM) & \(-0.4720\) & \(0.6426\) \\ EfficientNetB0-PCA (RF vs. SVM) & \(-1.2742\) & \(0.2188\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical analysis of classification algorithms for DermIS dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Algorithms** & **Mean Accuracy (\%)** & **95\% Confidence Intervals (\%)** \\ \hline EfficientNetB0-PCA DT & 90.40\% & (88.42\%, 92.38\%) \\ EfficientNetB0-PCA RF & 90.92\% & (89.24\%, 92.60\%) \\ EfficientNetB0-PCA NN & 90.93\% & (89.30\%, 92.56\%) \\ EfficientNetB0-PCA SVM & 91.00\% & (89.41\%, 92.59\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean accuracy and confidence intervals of classification algorithms for ISIC dataset.