  This paper proposes a weakly-supervised machine learning-based approach
aiming at a tool to alert patients about possible respiratory diseases. Various
types of pathologies may affect the respiratory system, potentially leading to
severe diseases and, in certain cases, death. In general, effective prevention
practices are considered as major actors towards the improvement of the
patient's health condition. The proposed method strives to realize an easily
accessible tool for the automatic diagnosis of respiratory diseases.
Specifically, the method leverages Variational Autoencoder architectures
permitting the usage of training pipelines of limited complexity and relatively
small-sized datasets. Importantly, it offers an accuracy of 57 %, which is in
line with the existing strongly-supervised approaches.
