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

In summary, DMD offers a promising alternative for public health professionals to capture underlying trends and potentially have lower computational demands compared to other machine learning models.

Authors

M. Courtney Hughes (ORCID: https://orcid.org/0000-0002-8699-5701)
Lei Guo (ORCID: https://orcid.org/0000-0003-2055-7618)
Jifu Tan (ORCID: https://orcid.org/0000-0002-8314-2017)

Institutions

Northern Illinois University (US)

Publication Details

Journal: Frontiers in Public Health
Published: 2025-04-25
DOI: https://doi.org/10.3389/fpubh.2025.1472398
Citations: 1
Primary Topic: Model Reduction and Neural Networks
Type: article
Field-Weighted Citation Impact: 1.32

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article

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

M. Courtney Hughes, Lei Guo, Jifu Tan

Apr 25, 2025

1 citations

Frontiers in Public Health

Model Reduction and Neural Networks

1.32

article

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

M. Courtney Hughes, Lei Guo, Jifu Tan

April 25, 2025article en

1 citations

Abstract

In summary, DMD offers a promising alternative for public health professionals to capture underlying trends and potentially have lower computational demands compared to other machine learning models.

Frontiers in Public HealthVol. 13

Northern Illinois University (US)

Openalex Percentile: Top 20%

Model Reduction and Neural Networks

1.32

DOI Open Access PDF OpenAlex

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Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction — M. Courtney Hughes, Lei Guo, et al. · Frontiers in Public Health (2025) | TGRS Research Map | TGRS