Drug Recommendation System on Sentiment Analysis of Drug Reviews by Using Machine learning

Abstract

Abstract: With the healthcare system facing increased challenges due to the COVID-19 pandemic, there's a big demand for new ideas to help doctors and nurses. This paper suggests a new way of using computers to help doctors decide which medicines to give to patients. By using smart computer programs, we can make it easier for healthcare workers to handle their workload and provide better care for patients. By analyzing patient reviews, we employ sentiment analysis using advanced vectorization methods like Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and Manual Feature Analysis. These techniques allow for the identification of subtle sentiment nuances crucial for tailoring personalized drug recommendations. We utilize various classification algorithms such as Naive Bayes, Support Vector Classifier (SVC), and Random Forest to predict sentiments. Among these, the LinearSVC classifier coupled with TF-IDF vectorization demonstrates superior performance in sentiment prediction. We tested our system using various measures like precision, recall, F1-score, and Area Under the Curve (AUC) to ensure its effectiveness. By incorporating advanced machine learning techniques, our system offers a strong foundation for enhancing the accuracy of drug prescriptions, which is a key issue in today's healthcare. Through better drug recommendations, our goal is to ultimately enhance patient health outcomes and play a part in advancing healthcare practices, especially in the face of complex and evolving healthcare challenges.