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The widespread adoption of machine-learning technologies has significantly influenced decision-making in various fields. In this research, we harness the power of machine learning, specifically the Light Gradient Boosting Machine (LGBM) classifier, to develop a predictive model. This model efficiently estimates the likelihood of specific outcomes using a predefined dataset (Covid-19) encompassing various features. Our aim is to provide a practical tool that aids in decision-making processes, offering valuable insights and supporting resource allocation strategies. While it’s essential to acknowledge that no predictive model is flawless, these tools hold substantial promise in enhancing our ability to make informed decisions and contribute to addressing complex challenges across diverse domains.

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Light Gradient Boosting Machine Algorithm (LGBM) for COVID-19 Prediction Based on Symptoms

  • 会议论文

  • 发表日期：

    2025年

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  • Sonika Malik,
  • Preeti Rathee

摘要

The widespread adoption of machine-learning technologies has significantly influenced decision-making in various fields. In this research, we harness the power of machine learning, specifically the Light Gradient Boosting Machine (LGBM) classifier, to develop a predictive model. This model efficiently estimates the likelihood of specific outcomes using a predefined dataset (Covid-19) encompassing various features. Our aim is to provide a practical tool that aids in decision-making processes, offering valuable insights and supporting resource allocation strategies. While it’s essential to acknowledge that no predictive model is flawless, these tools hold substantial promise in enhancing our ability to make informed decisions and contribute to addressing complex challenges across diverse domains.