Detection of heart murmurs from stethoscope sounds is a key clinical technique used to identify cardiac abnormalities. We describe the creation of an ensemble classifier using both deep and hand-crafted features to screen for heart murmurs and clinical abnormality from phonocardiogram recordings over multiple auscultation locations. The model was created by the team \textit{Murmur Mia!} for the George B. Moody PhysioNet Challenge 2022.
Methods: Recordings were first filtered through a gradient boosting algorithm to detect \textit{Unknown}. We assume that these are related to poor quality recordings, and hence we use input features commonly used to assess audio quality. Two further models, a gradient boosting model and ensemble of convolutional neural networks, were trained using time-frequency features and the mel-frequency cepstral coefficients (MFCC) as inputs, respectively. The models were combined using logistic regression, with bespoke rules to convert individual recording outputs to patient predictions. Results: On the challenge validation set, our classifier scored 0.737 for the weighted accuracy and 11828 for clinical outcome challenge metric. This placed 28/305 and 188/305 on the challenge leaderboard, for each scoring metric, respectively.