  Heart murmurs are a common manifestation of cardiovascular diseases and can
provide crucial clues to early cardiac abnormalities. While most current
research methods primarily focus on the accuracy of models, they often overlook
other important aspects such as the interpretability of machine learning
algorithms and the uncertainty of predictions. This paper introduces a heart
murmur detection method based on a parallel-attentive model, which consists of
two branches: One is based on a self-attention module and the other one is
based on a convolutional network. Unlike traditional approaches, this structure
is better equipped to handle long-term dependencies in sequential data, and
thus effectively captures the local and global features of heart murmurs.
Additionally, we acknowledge the significance of understanding the uncertainty
of model predictions in the medical field for clinical decision-making.
Therefore, we have incorporated an effective uncertainty estimation method
based on Monte Carlo Dropout into our model. Furthermore, we have employed
temperature scaling to calibrate the predictions of our probabilistic model,
enhancing its reliability. In experiments conducted on the CirCor Digiscope
dataset for heart murmur detection, our proposed method achieves a weighted
accuracy of 79.8% and an F1 of 65.1%, representing state-of-the-art results.
