# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 6 Experimental and Results
### Recognition Performance Evaluation  
In this section, we present the recognition results for breath sound classification, focusing on the most representative outcomes from various features and classifiers due to paper length constraints. We evaluate performance using accuracy, precision, recall, and F1 score, highlighting key results from features like Dynamic Linear Prediction Coefficients (DLPCs) and spectral entropy, paired with classifiers such as random forest and logistic regression. The confusion matrix tabulates predicted versus actual class labels, as shown in Table 2:  
Accuracy is the ratio of correctly classified instances to the total:  
\[\text{Accuracy}=\frac{\sum_{i=1}^{C}\text{TP}_{i}}{\text{Total Samples}} \tag{59}\]  
Recall is the ability of a classification model to identify all data points in a relevant class. \[\text{Precision}_{i}=\frac{\text{TP}_{i}}{\text{TP}_{i}+\text{FP}_{i}} \tag{60}\]  
Precision is the ability of a classification model to return only the data points in a class. \[\text{Recall}_{i}=\frac{\text{TP}_{i}}{\text{TP}_{i}+\text{FN}_{i}} \tag{61}\]  
The F1 score, combining precision and recall, assesses classification performance per class. \[\text{F1}_{i}=2\cdot\frac{\text{Precision}_{i}\cdot\text{Recall}_{i}}{\text{ Precision}_{i}+\text{Recall}_{i}} \tag{62}\]  
Classification results using K-Nearest Neighbor (KNN) with LPC, LPCC, MFCC, and DLPC features are detailed in Tables 3-6, respectively. Overall accuracies are 0.733 for LPC, 0.65 for LPCC, 0.80 for MFCC, and 0.717 for DLPC. MFCC outperforms other features for breath sound extraction, achieving a peak accuracy of 80%. LPC and DLPC yield similar results, around 70%, while LPCC performs worst at 65%. Notably, DLPC offers the advantage of reduced computational complexity despite its competitive accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predicted \(\downarrow\)/Actual \(\rightarrow\)** & **Asthma** & **Group** & **Pneumonia** & **Normal** \\ \hline Asthma & \(TP_{1}\) & \(FN_{12}\) & \(FN_{13}\) & \(FN_{14}\) \\ Group & \(FP_{21}\) & \(TP_{2}\) & \(FN_{23}\) & \(FN_{24}\) \\ Pneumonia & \(FP_{31}\) & \(FP_{32}\) & \(TP_{3}\) & \(FN_{34}\) \\ Normal & \(FN_{41}\) & \(FN_{42}\) & \(FN_{43}\) & \(TP_{4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for breath sound classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** \\ \hline Asthma & 0.867 & 0.765 & 0.812 \\ Group & 0.467 & 1.000 & 0.636 \\ Pneumonia & 0.700 & 0.808 & 0.751 \\ Normal & 0.900 & 0.587 & 0.710 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LPC: precision, recall, and F1 score values; accuracy = 0.73. For the hidden Markov model (HMM) classification, the data was partitioned into training and testing sets. The number of training iterations significantly impacted the classification performance. Figure 9 illustrates the HMM classification results, demonstrating the effect of varying training data proportions and training loop counts. The highest recognition rate, achieving 80%, was observed with 10 training loops and a 70% training data proportion. To evaluate the efficacy of artificial neural networks (ANNs) for breath sound recognition, we conducted experiments using Linear Predictive Coding (LPC) features. The classification accuracy, as a function of training data proportion and training iterations, is presented in Figure 10. Our analysis revealed that the neural network weights converged after approximately 50 iterations. Furthermore, satisfactory training performance was achieved with 50% to 60% of the total breath sound trainin \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** \\ \hline Asthma & 0.733 & 0.733 & 0.733 \\ Group & 0.667 & 0.667 & 0.667 \\ Pneumonia & 0.667 & 0.667 & 0.667 \\ Normal & 0.800 & 0.800 & 0.800 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DLPC: precision, recall, and F1 score values; accuracy = 0.717.