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An R-Based Landscape Validation of a Competing Risk Model

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Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey.

¹Department of Electrical and Electronics Engineering, Haliç University, Istanbul, Turkey.

|February 19, 2024

Summary

This summary is machine-generated.

Pandemic preparedness requires effective planning for hospital capacity and mortality. This study uses machine learning to accurately predict daily COVID-19 cases and deaths in Turkey, offering valuable insights for future health crises.

Keywords:

ARIMA Bagging regressor Boosting regressor Covid-19 pandemic Ensemble learning LSTM Machine learning Random forest regressor

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

Pandemics strain healthcare systems, causing critical shortages in hospital beds and intensive care units.
Effective planning is essential to manage patient flow and mortality during health crises.
Lessons learned from the COVID-19 pandemic can inform preparedness for future events.

Purpose of the Study:

To analyze the statistical properties of daily COVID-19 cases and deaths in Turkey.
To develop and compare machine learning models for predicting daily cases and deaths.
To evaluate model performance using various accuracy and error metrics.

Main Methods:

Statistical analysis of daily COVID-19 case and death data in Turkey.
Application of Random Forest Regression for prediction.
Comparison with seven other machine learning models: Bagging, AdaBoost, Gradient Boosting, XGBoost, Decision Tree, LSTM, and ARIMA.

Main Results:

Daily COVID-19 cases in Turkey exhibit non-stationary characteristics.
Random Forest Regression achieved 92.30% accuracy and an R² score of 0.9893 for daily case prediction.
Random Forest Regression achieved 91.39% accuracy and an R² score of 0.9834 for daily death prediction.
Bagging regressor showed comparable performance but with run-to-run variability.

Conclusions:

Machine learning models, particularly Random Forest Regression, can effectively predict COVID-19 daily cases and deaths.
Accurate prediction models are crucial for pandemic preparedness and resource allocation.
The study highlights the importance of robust and consistent predictive models in public health emergencies.