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 different ML diagnostic models and obtain the most valuable features for asthma diagnosis using data from the National Health and Nutrition Examination Survey (NHANES) dataset. A total of 8,888 participants with available asthma diagnosis data from the 2017-2018 NHANES survey were included. After careful selection of variables related to asthma, various ML algorithms including Support Vector Machine (SVM), Random Forest (RF), AdaBoost (ADA), XGBoost (XGB), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Mul

SUBMITTER: Kohandel Gargari O 

PROVIDER: S-EPMC11802912 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature