Classification of Sentiment Analysis Based on Machine Learning in Drug Recommendation Application

Since the corona virus was discovered, there has been an increase in the difficulty of gaining access to genuine clinical resources. This includes a scarcity of experts and healthcare workers and an absence of appropriate equipment and drugs. The whole medical community is in a state of crisis, which has led to the deaths of a significant number of people. Because the drug was not readily available, people began self-medicating without first consulting with their doctors, making their health situation much worse. Recently, machine learning has shown to be helpful in a wide variety of applications, and there has been an uptick in the amount of new work done for automation. The study’s goal is to showcase a medicine recommender system (RS) that can drastically cut down on the amount of labor now being done by specialists. In this research, we build a drug recommendation system by analyzing patient feedback for tone. We utilize the machine learning-based XGBoost classifier, count vectorization for feature extraction, and ADASYN for data balancing. This system can assist in recommending the best drug for a specific disease by using a variety of implementation processes. The predicted sentiments were established based on their precision, recall, accuracy, F1-score, and area under the curve (AUC). The findings suggest that 95% accuracy may be achieved using the classification algorithm XGBoost with count vectorization compared to other models. The results of our experiments demonstrate that our system can provide highly accurate, efficient, and scalable drug recommendations.