Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.

Journal: Frontiers in public health
PMID: 40352838

Abstract

INTRODUCTION: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools but can be computationally expensive and require specialized knowledge. Dynamic mode decomposition (DMD) is an alternative that offers efficient analysis with fewer resources. This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.

Authors

  • L Raymond Guo
    College of Health and Human Sciences, Northern Illinois University, Dekalb, IL, United States.
  • Jifu Tan
    Department of Mechanical Engineering, Northern Illinois University, DeKalb, IL, United States.
  • M Courtney Hughes
    Department of Public Health, Northern Illinois University, DeKalb, IL, USA.

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