Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning

TL;DR

This paper presents a machine learning-based drug recommendation system that analyzes patient reviews to predict sentiment and suggest appropriate medications, aiming to reduce the burden on healthcare professionals especially during crises like the COVID-19 pandemic.

Contribution

The study introduces a novel drug recommendation system utilizing sentiment analysis of patient reviews with multiple vectorization techniques and classification algorithms, highlighting the effectiveness of LinearSVC with TFIDF.

Findings

LinearSVC with TFIDF achieved 93% accuracy.

The system effectively predicts patient sentiment towards drugs.

Sentiment analysis can assist in drug recommendation during healthcare shortages.

Abstract

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.