Predicting Respiratory Diseases from Lung Sounds Using Machine Learning

Abstract

Respiratory diseases are a leading cause of death worldwide. Using machine learning for diagnosis could significantly reduce costs and radiation exposure due to X-ray and CT scans, and improve accessibility to places with limited technology or less-experienced staff. While similar technologies have been successfully applied in the medical field before, sound signal analysis is still in its early stages with significant potential. This thesis’s goal was to create a codebase to help researchers enter and advance the field of respiratory sound analysis. In total, six experiments were conducted with four classical machine learning and one deep learning algorithm. The aim was to classify six classes (five respiratory diseases and one class for healthy patients) using a database of respiratory sounds and patient data. Test results, which used macro-averaged F1-scores as the primary evalua-tion metric, showed that SVM and decision tree models worked best (scores 0.62 and 0.54), while the convolutional neural network models performed worst (best score 0.3). The diffe-rences in the models’ performances were most likely affected by the dataset’s noisiness and umbalancedness. Further research and better data would be required for any conclusive re-sults. The source code for this thesis is publicly available in a Github repository [1].