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Predicting asthma using imbalanced data modeling techniques: Evidence from 2019 Michigan BRFSS data.

Nirajan Budhathoki1, Ramesh Bhandari2, Suraj Bashyal3

  • 1Department of Statistics, Actuarial & Data Sciences, Central Michigan University, Mount Pleasant, Michigan, United States of America.

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Summary

Machine learning models effectively predicted asthma in Michigan adults using survey data. Key risk factors identified include chronic obstructive pulmonary disease, lower income, and female sex, guiding targeted interventions.

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Area of Science:

  • Public Health
  • Epidemiology
  • Data Science

Background:

  • Asthma prevalence and risk factors vary significantly by region due to environmental and socioeconomic differences.
  • Michigan exhibited higher asthma prevalence than the national average in 2019, necessitating state-specific analysis.
  • Traditional analysis methods can struggle with imbalanced datasets common in health surveys.

Purpose of the Study:

  • To predict asthma prevalence among Michigan adults using machine learning techniques.
  • To identify key risk factors associated with asthma in the Michigan adult population.
  • To compare the effectiveness of synthetic data generation techniques (ROSE and SMOTE) for imbalanced asthma data.

Main Methods:

  • Utilized the 2019 Behavioral Risk Factor Surveillance System (BRFSS) data for Michigan adults.
  • Applied machine learning algorithms, including logistic regression, LASSO, and gradient boosting.
  • Employed Random Over-Sampling Examples (ROSE) and Synthetic Minority Over-Sampling Technique (SMOTE) to handle data imbalance.

Main Results:

  • Both ROSE and SMOTE improved machine learning model performance, with ROSE showing superior results.
  • Logistic regression, partial least squares, gradient boosting, LASSO, and elastic net demonstrated comparable performance (AUC ~63%).
  • Identified risk factors: COPD, lower income, female sex, financial barriers to care, recent flu vaccination, young adult age (18-24), Black non-Hispanic ethnicity, and diabetes.

Conclusions:

  • Machine learning, combined with imbalanced data techniques, is effective for asthma prediction in large survey datasets.
  • Findings can inform early screening strategies for individuals at high risk of asthma.
  • Results provide a basis for developing targeted public health interventions to improve asthma care.