Pulmonary Disease Classification on Electrocardiograms Using Machine Learning
Pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma are among the leading causes of death in the US. These lung diseases often are diagnosed by pulmonologists using physical exam (e.g., lung auscultation) and objective measurement of lung function with pulmona...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135547 |
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| Summary: | Pulmonary diseases, such as chronic obstructive pulmonary
disease (COPD) and asthma are among the leading causes of
death in the US. These lung diseases often are diagnosed by
pulmonologists using physical exam (e.g., lung auscultation)
and objective measurement of lung function with pulmonary
function testing (PFT). These extensive tests, however, can
be inaccessible to many patients due to limited resources and
availability. In this paper, we explore the use of the easily accessible
electrocardiograms (ECGs) to train machine learning
models to classify pulmonary diseases. To this end, we developed
and experimented with two approaches: deep neural
networkmodels trained (e.g., Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs)) on ECG
signals directly, and non-neural models (e.g., support vector
machines (SVMs) and logistic regression) trained on derived
features from ECGs. In the task of classifying whether a patient
has obstructive lung disease, our results show that deep
neural network models outperformed the non-neural models,
though the difference is within 3% on accuracy and F1-score
metrics. |
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| ISSN: | 2334-0754 2334-0762 |