Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset

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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Bibliographic Details
Main Authors: Omid Kohandel Gargari, Mobina Fathi, Shahryar Rajai Firouzabadi, Ida Mohammadi, Mohammad Hossein Mahmoudi, Mehran Sarmadi, Arman Shafiee
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Asthma
Machine learning
Support vector machine
Bronchitis
Online Access:https://doi.org/10.1038/s41598-025-88345-1
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