Exploring Drug Recommendations Through Sentiment Analysis: A Machine Learning Perspective

Current drug recommendation systems often rely on clinical trials and general guidelines, which may not fully account for individual patient experiences. This study addresses the gap by leveraging sentiment analysis of patient reviews to enhance drug recommendations. The primary objective is to develop a machine learning-based drug recommendation system that incorporates sentiment from drug reviews, improving the personalization of drug recommendations. Using a dataset from UCI ML repository with over 215,000 instances, we apply sentiment analysis and various feature extraction techniques, including TF-IDF and Word2Vec, alongside machine learning classifiers to predict drug efficacy for conditions such as acne, hypertension, and depression. By integrating sentiment-based insights, this approach aims to provide more tailored recommendations for individual patients. Our results show that models like Linear SVC (TF-IDF) and Random Forest (manual features) outperform traditional methods, achieving up to 93% accuracy. These findings demonstrate the potential of sentiment analysis to enhance the relevance and accuracy of drug recommendations. This research contributes to the growing field of personalized healthcare by introducing a data-driven framework that captures patient sentiment, offering more accurate and individualized medication guidance.