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Related Experiment Videos

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly.

Hari Singh1, Seema Bawa2

  • 1Computer Science and Engineering Department, Jaypee University of Information Technology, Solan, Waknaghat, India.

Multimedia Systems
|May 12, 2021
PubMed
Summary
This summary is machine-generated.

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This study modeled COVID-19 deaths in the USA using regression techniques. Polynomial regression (PR) models offered better accuracy and lower errors than linear regression (LR) when considering multiple parameters.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Machine Learning

Background:

  • Predicting infectious disease mortality is crucial for public health.
  • Regression models are valuable tools for analyzing epidemiological data.
  • Accurate forecasting of COVID-19 deaths aids in resource allocation and policy development.

Purpose of the Study:

  • To develop and evaluate a regression model for predicting COVID-19 deaths in the United States.
  • To compare the performance of linear regression (LR), multi-linear regression (MLR), and polynomial regression (PR) models.
  • To identify the best-fitting regression technique based on statistical error metrics.

Main Methods:

  • Application of linear regression (LR), multi-linear regression (MLR), and polynomial regression (PR) techniques.
Keywords:
AccuracyCOVID-19Linear regressionMachine learningPolynomial regressiont-Test

Related Experiment Videos

  • Data preprocessing including analysis, cleaning, and debate of datasets.
  • Model evaluation using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and maximum likelihood ratio.
  • Statistical verification using the t-test and correlation analysis via heatmap and Pearson correlation matrix.

    • Main Results:

    • Linear regression (LR) showed good fit with all parameters but higher RMSE and MAE compared to PR.
    • High-degree polynomial regression (PR) models were needed for datasets with fewer independent parameters.
    • Low-degree polynomial regression (PR) models best-fitted the dataset when parameters from all dimensions were included.

    Conclusions:

    • Polynomial regression (PR) models demonstrate superior performance in predicting COVID-19 deaths compared to linear regression (LR).
    • The degree of the polynomial regression model should be selected based on the number of independent parameters considered.
    • The Li-MuLi-Poly model provides a robust framework for epidemiological mortality prediction.