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

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. Methods: We analyzed lung cancer incidence data (2000-2021) from 1,013 US counties. Machine learning models (random forest, gradient boosting machine, support vector machine) were trained and optimized on the training data. We also employed time series, a linear regression model, and DMD for comparison. All models were evaluated based on their ability to predict 2021 lung cancer incidence rates. Results: The time series model achieved the lowest root mean squared error, followed by random forest. Meanwhile, DMD had an RMSE similar to that of Random Forest. Nearly all counties in Kentucky had higher lung cancer incidence rates, while states like California, New Mexico, Utah, and Idaho showed lower trends. Conclusion: 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

Biology · Cancer · Data mining · Decomposition · Dynamic mode decomposition · Lung cancer · Machine learning · Computer Science · Machine Learning in Healthcare · Mathematics · Medicine · Pneumonia and Respiratory Infections · Sepsis Diagnosis and Treatment · Internal Medicine · Oncology

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