Deep Learning in Heart Murmur Detection: Analyzing the Potential of FCNN vs. Traditional Machine Learning Models

This research investigates the performance of machine learning and deep learning models in detecting heart murmurs from audio recordings. Using the PhysioNet Challenge 2016 dataset, we compare several traditional machine learning models—Support Vector Machine, Random Forest, AdaBoost, and Decision Tree—with a Fully Convolutional Neural Network (FCNN). The findings indicate that while traditional models achieved accuracies between 0.85 and 0.89, they faced challenges with data complexity and maintaining a balance between precision and recall. Ensemble methods such as Random Forest and AdaBoost demonstrated improved robustness but were still outperformed by deep learning approaches. The FCNN model, leveraging artificial intelligence, significantly outperformed all other models, achieving an accuracy of 0.99 with a precision of 0.94 and a recall of 0.96. These results highlight the potential of AI-driven cardiovascular diagnostics, as deep learning models exhibit superior capability in identifying intricate patterns in heart sound data. Our findings suggest that deep learning models offer substantial advantages in medical diagnostics, particularly for cardiovascular diagnostics, by providing scalable and highly accurate tools for heart murmur detection. Future work should focus on improving model interpretability and expanding dataset diversity to facilitate broader adoption in clinical settings.

@article{Hussein2025,
  title     = {Deep Learning in Heart Murmur Detection: Analyzing the Potential of FCNN vs. Traditional Machine Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Hajer Sayed Hussein and Hussein AlBazar and Roxane Elias Mallouhy and Fatima Al-Hebshi},
  doi       = {10.14569/IJACSA.2025.01602128},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602128}
}