Drug Prescribing System Using Patient Reviews Based on Sentimental Analysis

Abstract

The Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Random Forest is a novel approach aimed at assisting healthcare professionals in making informed decisions regarding drug prescriptions. This system leverages the power of sentiment analysis to analyze and extract valuable insights from large volumes of user-generated drug reviews. Sentiment analysis plays a crucial role in understanding the opinions, emotions, and experiences expressed by patients in their reviews. By classifying these sentiments as positive, negative, or neutral, the system provides a quantitative measure of the overall sentiment associated with a particular drug. This information can then be utilized to generate personalized drug recommendations tailored to individual patients' needs and preferences. The proposed system employs the Random Forest algorithm, a powerful machine learning technique, to perform sentiment analysis on drug reviews. Random Forest utilizes an ensemble of decision trees to classify sentiments based on a variety of features extracted from the reviews, such as keyword frequencies, sentence structures, and context. By aggregating the results of multiple decision trees, Random Forest achieves high accuracy and robustness in sentiment classification. To build the Drug Recommendation System, a comprehensive dataset of drug reviews is collected from various online sources. The dataset is pre-processed to remove noise, perform text normalization, and extract relevant features. Then, the Random Forest algorithm is trained on this processed dataset, using labelled reviews as input and their corresponding sentiments as output. The trained model is subsequently used to analyse new, unseen drug reviews and predict their sentiments. Once the sentiment analysis is performed, the system combines the sentiment scores with additional factors, such as drug efficacy, safety, side effects, and patient demographics, to generate personalized drug recommendations. These recommendations are based on a similarity measure that matches patients with similar profiles and preferences to those who have reported positive experiences with certain drugs.