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An empowered AdaBoost algorithm implementation: A COVID-19 dataset study.

Ender Sevinç1

  • 1Ankara Science University, Ankara, Turkey.

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|January 11, 2022
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This study introduces an improved machine learning model to predict Covid-19 patient severity using adaptive boost and decision trees. The new parameter tuning method enhances prediction accuracy, offering a promising approach for disease management.

Keywords:
Adaptive boost algorithmArtificial intelligenceCovid-19Machine learning

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Area of Science:

  • Computational biology
  • Medical informatics
  • Machine learning applications in healthcare

Background:

  • The Covid-19 pandemic has caused millions of deaths globally, necessitating advanced tools for patient management.
  • Predicting disease severity is crucial for effective resource allocation and treatment strategies.

Purpose of the Study:

  • To develop an improved machine learning model for predicting Covid-19 patient severity.
  • To enhance prediction accuracy by optimizing machine learning algorithms and parameter tuning.

Main Methods:

  • Utilized an adaptive boost algorithm with a decision tree estimator.
  • Implemented a novel parameter tuning process to reduce randomness and improve model performance.
  • Evaluated the model on UCI datasets and a specific Covid-19 dataset.

Main Results:

  • The proposed model demonstrated promising learning ratios after extensive experimentation with varied parameters.
  • Achieved competitive accuracy results when compared to state-of-the-art algorithms.
  • The new parameter tuning process proved effective in reducing the impact of random parameter selection.

Conclusions:

  • The developed machine learning model shows significant potential for accurately predicting Covid-19 patient severity.
  • This study highlights the utility of advanced machine learning techniques in addressing critical public health challenges.
  • Further research into advanced machine learning approaches for disease prediction is warranted.