Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning
This addresses the shortage of medical specialists by automating drug recommendations, though it is incremental as it applies existing machine learning methods to a new domain.
The paper tackles the problem of drug recommendation by building a system that uses sentiment analysis of patient reviews to recommend top drugs for a given disease, achieving 93% accuracy with LinearSVC and TFIDF vectorization.
Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists, healthcare workers, lack of proper equipment and medicines. The entire medical fraternity is in distress, which results in numerous individuals demise. Due to unavailability, people started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. This paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TFIDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. The predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. The results show that classifier LinearSVC using TFIDF vectorization outperforms all other models with 93% accuracy.