# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 6 Experimental and Results
### Recognition Performance Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** \\ \hline Asthma & 0.633 & 0.613 & 0.623 \\ Croup & 0.567 & 0.586 & 0.576 \\ Pneumonia & 0.600 & 0.900 & 0.720 \\ Normal & 0.800 & 0.600 & 0.686 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LPCC: precision, recall, and F1 score values; accuracy = 0.65. Figure 9: Accuracy of HMM classifier vs. training loops for different training data portions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** \\ \hline Asthma & 0.633 & 0.613 & 0.623 \\ Croup & 0.567 & 0.586 & 0.576 \\ Pneumonia & 0.600 & 0.900 & 0.720 \\ Normal & 0.800 & 0.600 & 0.686 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MFCC: precision, recall, and F1 score values; accuracy = 0.80.  
classification rate of approximately 70%. Subsequently, we investigated the performance of ANNs using Mel-Frequency Cepstral Coefficients (MFCCs) as input features. Again, we varied the training data proportion and the number of training iterations. In this configuration, neural network convergence was observed after roughly 50 iterations, and a training dataset comprising 70% to 80% of the total data yielded optimal results. While the classification rate using MFCC features alone was approximately 70%, the highest achieved classification rate was 83.3%, indicating potential for improved performance with optimized training parameters. For artificial neural networks (ANNs) paired with LPC, accuracy remains relatively low, below 0.70, with no discernible trend of improvement from additional iterations or increased data sample size, the latter limited by our dataset. This suggests that ANNs and LPC form a suboptimal combination for breath sound classification. In contrast, when ANNs are paired with MFCCs, increasing the data sample size boosts accuracy, and additional iterations further enhance performance, with some configurations achieving 0.80 or higher. This implies that ANNs and MFCCs offer promising synergy for effective breath sound recognition, leveraging larger datasets and iterative refinement. Tables 7-9 present the performance of spectral entropy features across different classifiers--K-Nearest Neighbor (KNN), random forest (RF), and logistic regression (LR)--evaluated using accuracy, precision, recall, and F1 score for the classes asthma, group, normal, and pneumonia. KNN and RF achieve near-perfect accuracy (97%), with exceptional performance on normal and pneumonia (F1 scores of 1.0). This reflects spectral entropy's ability to capture distinct patterns in these conditions. However, slight trade-offs emerge for asthma (precision 0.91; recall 1.0) and group (precision 1.0; recall 0.9 for KNN; 0.88 recall for RF), suggesting minor variability in detecting these disorders. In contrast, logistic regression performs poorly, with an accuracy of 63% and significant weakness in classifying pneumonia (F1 0.29; precision 0.33; recall 0.25), indicating that spectral entropy's nonlinear properties are less effectively leveraged by LR's linear framework. These results underscore spectral entropy's critical role in enhancing the discriminative power of KNN and RF, making them robust choices for breath sound classification, while highlighting LR's limitations in this context. Figure 10: Accuracy of ANN classifier vs. training loops for different training data portions. The Table 10 presents the [PERSON] 95% confidence intervals (CIs) for classification accuracy across different combinations of audio features and classifiers used in breath sound analysis. The confidence interval provides an estimated range within which the true accuracy lies, with 95% certainty. The DLPC and MFCC with KNN yielded high performance, with the DLPC achieving a CI of \([0.717,0.866]\), demonstrating strong reliability in classifying pediatric respiratory sounds. Spectral entropy, when used with KNN and random forest, achieved the highest confidence intervals of \([0.925,0.990]\), indicating promising predictive consistency.