AFEN: Respiratory Disease Classification using Ensemble Learning
- URL: http://arxiv.org/abs/2405.05467v1
- Date: Wed, 8 May 2024 23:50:54 GMT
- Title: AFEN: Respiratory Disease Classification using Ensemble Learning
- Authors: Rahul Nadkarni, Emmanouil Nikolakakis, Razvan Marinescu,
- Abstract summary: We present AFEN (Audio Feature Learning), a model that leverages Convolutional Neural Networks (CNN) and XGBoost.
We use a meticulously selected mix of audio features which provide the salient attributes of the data and allow for accurate classification.
We empirically verify that AFEN sets a new state-of-theart using Precision and Recall as metrics, while decreasing training time by 60%.
- Score: 2.524195881002773
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present AFEN (Audio Feature Ensemble Learning), a model that leverages Convolutional Neural Networks (CNN) and XGBoost in an ensemble learning fashion to perform state-of-the-art audio classification for a range of respiratory diseases. We use a meticulously selected mix of audio features which provide the salient attributes of the data and allow for accurate classification. The extracted features are then used as an input to two separate model classifiers 1) a multi-feature CNN classifier and 2) an XGBoost Classifier. The outputs of the two models are then fused with the use of soft voting. Thus, by exploiting ensemble learning, we achieve increased robustness and accuracy. We evaluate the performance of the model on a database of 920 respiratory sounds, which undergoes data augmentation techniques to increase the diversity of the data and generalizability of the model. We empirically verify that AFEN sets a new state-of-the-art using Precision and Recall as metrics, while decreasing training time by 60%.
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