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Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Updated: Dec 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine learning-based mortality rate prediction using optimized hyper-parameter.

Y A Khan1, S Z Abbas2, Buu-Chau Truong3

  • 1School of Statistics, Jiangxi University of Finance and Economics, Nanchang, China; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.

Computer Methods and Programs in Biomedicine
|September 5, 2020
PubMed
Summary
This summary is machine-generated.

This study predicts COVID-19 mortality rates using regression techniques. Sweden

Keywords:
Covid-19 deaths rateHyper-parameterMortality rateOptimizationPrediction

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Area of Science:

  • Epidemiology and Public Health
  • Machine Learning in Healthcare

Background:

  • The COVID-19 pandemic necessitates advanced prediction models beyond historical data extrapolation.
  • Existing models often rely on past disease parameters, limiting their predictive accuracy for evolving pandemics.

Purpose of the Study:

  • To predict COVID-19 mortality rates using regression techniques.
  • To compare prediction models across five countries: France, Spain, Turkey, Sweden, and Pakistan.
  • To identify effective strategies for controlling mortality rates despite rising confirmed cases.

Main Methods:

  • Development of regression models utilizing machine learning techniques.
  • Optimization of hyperparameters for enhanced model performance.
  • Training models on confirmed cases data as a primary predictor variable.

Main Results:

  • Models were constructed for France, Spain, Turkey, Sweden, and Pakistan using confirmed cases data.
  • Sweden demonstrated a lower death rate with over 20,000 confirmed cases, notably without implementing a lockdown.
  • The Gaussian Process Regression (GPR) method indicated a high mortality rate and low Root Mean Square Error (RMSE) for Pakistan.

Conclusions:

  • The mortality rate-based prediction (MRP) model using GPR is recommended for COVID-19 in Pakistan.
  • Adopting strategies similar to Sweden's approach can effectively control mortality rates.
  • The Swedish model is identified as the most effective for managing COVID-19 mortality.