Summary of Machine Learning-based Algorithms For At-home Respiratory Disease Monitoring and Respiratory Assessment, by Negar Orangi-fard et al.
Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment
by Negar Orangi-Fard, Alexandru Bogdan, Hersh Sagreiya
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes developing machine learning-based algorithms for at-home respiratory disease monitoring and assessment of patients undergoing continuous positive airway pressure (CPAP) therapy. The authors trained various machine learning models, including the random forest classifier, logistic regression, and support vector machine (SVM), to predict breathing types based on data from 30 healthy adults. The random forest classifier demonstrated high accuracy, particularly when incorporating breathing rate as a feature. This work has the potential to transition respiratory assessments from clinical settings to home environments, enhancing accessibility and patient autonomy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to create machine learning algorithms that can help people with respiratory diseases monitor their condition at home instead of in hospitals. The researchers trained different types of AI models using data from healthy adults and found that one type was very good at predicting how people were breathing based on certain features like breathing rate. This could make it easier for people to track their health and get better care. |
Keywords
» Artificial intelligence » Logistic regression » Machine learning » Random forest » Support vector machine
