Drug Recommendation system based on Medical Condition Classification and Sentiment Analysis of Drug Reviews

The steady growth of the internet has increased the amount of user generated data on the web. In the healthcare do-main, patients now commonly post their reviews about medicines after consuming them to create public awareness. Natural Language Processing techniques significantly contribute to the medical field by analyzing these public reviews and identifing the effectiveness of drugs as well as understanding medical conditions they are suffering from which will help healthcare professionals and pharmacovigilance systems to ensure the physical and mental well being of the patients. Hence, this research endeavors to develop a comprehensive framework for patient medical con-dition classification, sentiment prediction from patients reviews and recommend suitable medicines to them. Four algorithms: Multinomial Na¨ıve Bayes, Passive Aggressive Classifier, SGD Classifier and MLP Classifier have been applied to medical condition classification and two algorithms: Multinomial Na¨ıve Bayes and Logistic Regression have been applied for sentiment prediction. The results demonstrate that the proposed framework has an accuracy of 94.4% for Passive Aggressive Classifier in medical condition classification and accuracy of 94.85% for Logistic Regression in sentiment prediction.