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Analysis of COVID-19 Death Cases Using Machine Learning.

Humaira Aslam1, Santanu Biswas1,2

  • 1Department of Mathematics, Adamas University, Barasat-Barrackpore Road, Jagannathpur, Kolkata, West Bengal 700126 India.

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Machine learning models accurately forecast COVID-19 death cases by analyzing risk factors. This research helps predict mortality rates during epidemics like the Novel Coronavirus pandemic.

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Machine learningPrediction

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • The COVID-19 pandemic has caused millions of deaths globally.
  • Assessing COVID-19 severity and predicting mortality are crucial for public health.
  • Understanding risk factors influencing COVID-19 mortality requires further investigation.

Purpose of the Study:

  • To develop and evaluate machine learning models for forecasting COVID-19 death cases.
  • To identify key risk factors significantly impacting COVID-19 mortality rates.
  • To establish a real-time forecasting system for COVID-19 deaths.

Main Methods:

  • Utilized various regression machine learning models, including XGBoost, Random Forest, and Support Vector Machines (SVM).
  • Employed an optimal regression tree algorithm to determine the impact of causal variables on mortality.
  • Analyzed datasets from the US, India, Italy, Asia, Europe, and North America.

Main Results:

  • Machine learning models demonstrated effectiveness in forecasting COVID-19 death cases.
  • Identified significant causal variables influencing COVID-19 mortality rates.
  • The proposed models provide a reliable method for near-future epidemic death case prediction.

Conclusions:

  • Machine learning techniques are valuable tools for understanding and predicting COVID-19 mortality.
  • The study provides a framework for real-time forecasting of deaths during pandemics.
  • Further research can enhance predictive accuracy and identify novel risk factors.