## 4 Results and Discussion
### Result Comparison  
The present study compared the accuracy, specificity, and sensitivity of different combinations of percussive and harmonic features, as illustrated in Table 12. However, based on the ROC-AUC feature selection method, the findings demonstrate that the chroma-based harmonic features yield the most favorable outcomes. Comprising only 12 attributes, they still demonstrated remarkable efficiency, achieving a classification accuracy of 93.40%, sensitivity of 82.40%, and specificity of 80.60%. This reduced feature set notably lowers the computational load, enabling faster training and testing while maintaining diagnostic reliability. The ANN model achieved up to 93.80% accuracy, with 82.50% sensitivity and 82.40% specificity, using the full harmonic feature set, which comprised 82 features, underscoring the robustness of the selected attributes. However, using a reduced feature set, particularly focusing on the 12 chroma features, provides significant computational benefits, especially during real-time testing and application in clinical settings. Through focusing on a smaller number of features, the computational load decreases, making the model more efficient and suitable for deployment on devices with limited processing capabilities, such as mobile health monitoring tools or embedded systems. This efficiency can lead to faster processing times, allowing for rapid and near-real-time classification of heart sounds [40]. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & **Count** & **Acc.** & **Results** & **Precision** & **Recall** & **F1-Score** \\ \hline All Percussive & 82 & 90.60\% & Normal & 0.93 & 0.96 & 0.94 \\  & & & Abnormal & 0.79 & 0.68 & 0.73 \\ \hline Chroma only & 12 & 80.50\% & Normal & 0.81 & 0.99 & 0.89 \\  & & & Abnormal & 0.35 & 0.03 & 0.05 \\ \hline Chroma CENS & 12 & 80.30\% & Normal & 0.82 & 0.97 & 0.89 \\ only & & & Abnormal & 0.42 & 0.10 & 0.16 \\ \hline MFCC only & 40 & 90.70\% & Normal & 0.93 & 0.95 & 0.94 \\  & & & Abnormal & 0.78 & 0.71 & 0.74 \\ \hline Statistical only & 18 & 84.60\% & Normal & 0.87 & 0.96 & 0.91 \\  & & & Abnormal & 0.68 & 0.37 & 0.48 \\ \hline Chroma, Statistical & 30 & 85.80\% & Normal & 0.88 & 0.96 & 0.92 \\  & & & Abnormal & 0.72 & 0.42 & 0.53 \\ \hline Chroma CENS, Statistical & 30 & 80.30\% & Normal & 0.88 & 0.96 & 0.92 \\  & & & Abnormal & 0.73 & 0.45 & 0.56 \\ \hline MFCC, Statistical & 58 & 91.00\% & Normal & 0.92 & 0.97 & 0.95 \\  & & & Abnormal & 0.83 & 0.66 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experiment results from percussive features. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Features Set (Count)** & **True** & **True** & **False** & **False** \\  & **Positives** & **Negatives** & **Positives** & **Negatives** \\ \hline All Features & 2780 & 467 & 122 & 216 \\ Chroma & 2865 & 20 & 37 & 663 \\ Chroma CENS & 2811 & 66 & 91 & 617 \\ MFCC & 2766 & 484 & 136 & 199 \\ Statistical & 2783 & 250 & 119 & 433 \\ Chroma \& Statistical & 2788 & 289 & 114 & 394 \\ Chroma CENS and Statistical & 2811 & 66 & 91 & 617 \\ MFCC \& Statistical & 2808 & 454 & 94 & 229 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results: model validation results of percussive features. Moreover, the chroma features mitigate this limitation by effectively representing harmonic content from the PCG signals. Chroma features focus on the pitch content and emphasize harmonics that are crucial for distinguishing between normal and abnormal heart sounds. This makes them particularly effective in capturing the key spectral qualities required for accurate classification, even with a reduced set of features. Additionally, removing low-quality signals and balancing class distributions further enhanced the model's effectiveness in real-world scenarios. This study emphasizes the potential for deploying such computationally efficient, non-invasive diagnostic models in resource-constrained environments, paving the way for advancements in cardiac health monitoring.