  Today, data collection has improved in various areas, and the medical domain
is no exception. Auscultation, as an important diagnostic technique for
physicians, due to the progress and availability of digital stethoscopes, lends
itself well to applications of machine learning. Due to the large number of
auscultations performed, the availability of data opens up an opportunity for
more effective analysis of sounds where prognostic accuracy even among experts
remains low. In this study, digital 6-channel auscultations of 45 patients were
used in various machine learning scenarios, with the aim of distinguishing
between normal and anomalous pulmonary sounds. Audio features (such as
fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive
statistics of log energy, RMS and MFCC) were extracted using the Python library
Surfboard. Windowing and feature aggregation and concatenation strategies were
used to prepare data for tree-based ensemble models in unsupervised (fair-cut
forest) and supervised (random forest) machine learning settings. The
evaluation was carried out using 9-fold stratified cross-validation repeated 30
times. Decision fusion by averaging outputs for a subject was tested and found
to be useful. Supervised models showed a consistent advantage over unsupervised
ones, achieving mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score
0.771) in side-based detection and mean AUC ROC of 0.721 (accuracy 68.89%,
Kappa 0.371, F1-score 0.650) in patient-based detection.
