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The effective handling of biomedical waste is an important issue for preventing the spread of infectious diseases. This will safeguard society and protect the environment. The improper handling of healthcare waste at the generator site before transportation is a serious issue, posing greater threats such as infectious disease outbreaks and others. Handling this waste effectively should be the prime concern of all stakeholders to prevent environmental damage and build a safe society. This study proposes an automated waste management system that works on supervised machine and deep learning algorithms that segregate waste according to the standard segregation protocol. This system categorizes the waste into Red, Yellow, Blue, and Black categories according to its type. The machine learning algorithms used in this system are Decision Tree, Random Forest, and Transfer Learning, and the study is validated with a Convolutional Neural Network, a deep learning algorithm. The Decision Tree algorithm classifies medical waste with the highest accuracy of 99.42% over others.

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An AI-Driven Approach Using Machine Learning and Deep Learning for Effective Healthcare Waste Management

  • 会议论文

  • 发表日期：

    2026年

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  • Ananya Kapoor,
  • Vaibhav Sharma,
  • Rajeev Agrawal

摘要

The effective handling of biomedical waste is an important issue for preventing the spread of infectious diseases. This will safeguard society and protect the environment. The improper handling of healthcare waste at the generator site before transportation is a serious issue, posing greater threats such as infectious disease outbreaks and others. Handling this waste effectively should be the prime concern of all stakeholders to prevent environmental damage and build a safe society. This study proposes an automated waste management system that works on supervised machine and deep learning algorithms that segregate waste according to the standard segregation protocol. This system categorizes the waste into Red, Yellow, Blue, and Black categories according to its type. The machine learning algorithms used in this system are Decision Tree, Random Forest, and Transfer Learning, and the study is validated with a Convolutional Neural Network, a deep learning algorithm. The Decision Tree algorithm classifies medical waste with the highest accuracy of 99.42% over others.