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In modern healthcare, personalized and efficient drug prescription is imperative. This introduces a drug recommendation system using the UCI ML Drug-Review dataset to enhance prescription precision. The system employs machine learning and data analytics to analyze patient-specific information from drug reviews. Through advanced NLP and sentiment analysis, it extracts valuable insights, identifying patterns in patient experiences. The system tailors recommendations based on similarities between patients with comparable characteristics and treatment responses. Rigorous training and testing optimize its performance in predicting suitable drugs for various patient profiles. Algorithms like Support Vector Machine, Random Forest, Logistic Regression, and Gradient Boosted Trees are used, with evaluation metrics such as precision, recall, and F1 score. This system promises to enhance clinical decision-making, reduce adverse drug reactions, and improve patient care by integrating real-world patient experiences from the UCI ML Drug-Review dataset, advancing personalized medicine.

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Drug Recommendation System Based on Patient Condition Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Kanuri Dilip Kumar,
  • C. S. Pavan Kumar,
  • Bondalapati Mahesh

摘要

In modern healthcare, personalized and efficient drug prescription is imperative. This introduces a drug recommendation system using the UCI ML Drug-Review dataset to enhance prescription precision. The system employs machine learning and data analytics to analyze patient-specific information from drug reviews. Through advanced NLP and sentiment analysis, it extracts valuable insights, identifying patterns in patient experiences. The system tailors recommendations based on similarities between patients with comparable characteristics and treatment responses. Rigorous training and testing optimize its performance in predicting suitable drugs for various patient profiles. Algorithms like Support Vector Machine, Random Forest, Logistic Regression, and Gradient Boosted Trees are used, with evaluation metrics such as precision, recall, and F1 score. This system promises to enhance clinical decision-making, reduce adverse drug reactions, and improve patient care by integrating real-world patient experiences from the UCI ML Drug-Review dataset, advancing personalized medicine.