Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset
Abstract Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML) offers a promising avenue for addressing these challenges by leveraging demographic and clinical data. In this study, we aim to compare diff...
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Main Authors: | Omid Kohandel Gargari, Mobina Fathi, Shahryar Rajai Firouzabadi, Ida Mohammadi, Mohammad Hossein Mahmoudi, Mehran Sarmadi, Arman Shafiee |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
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Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-88345-1 |
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