  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.
