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Medicine Recommendation System using Machine Learning

  • Unique Paper ID: 181812
  • Volume: 12
  • Issue: 1
  • PageNo: 5516-5521
  • Abstract:
  • The core objective of the project is to develop a machine learning-based disease prediction and medicine recommendation website capable of harnessing individual health data to generate personalized health recommendations, fostering early detection and more effective management of health issues. Utilizing machine learning (ML), the team aims to predict diseases like joint pain, burning micturition, abdominal pain, and irregular sugar levels for early intervention and adapting diagnosis strategies. In their approach, ML algorithms, including the gradient boosting algorithm (GBA), analyze diverse health data sources to build a comprehensive recommendation system. All models demonstrate an accuracy rate of over 100%, highlighting the system's reliability and effectiveness. By integrating various health data sources and focusing on proactive health management, this initiative has the potential to transform health practices. It empowers individuals to make informed decisions regarding their well-being and fosters improved health outcomes.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 5516-5521

Medicine Recommendation System using Machine Learning

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8.01 (Year 2024)
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Journal no 47859

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