VOICE ASSISTANT DRUG RECOMMENDATION

Abstract

Since the coronavirus emerged, the shortage of appropriate clinical resources—such as specialists, care personnel, the right equipment and medications, etc. has reached an all-time high. The medical profession as a whole is in difficulty, which leads to many people passing away. Due to inconvenience, people began taking medications frequently without getting proper consultation, which made their health situation worse than usual. Recently, machine learning has proven useful in many areas, and creative work for automation is on the rise. It aims to provide a drug recommendation system that will significantly reduce the workload of doctors. We are developing a method for prescribing medications that analyzes patient feedback to forecast the emotional response to various vectorization techniques, including Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which may facilitate suggest the highest drug for a given sickness by totally different classification algorithms. The anticipated sentiments were evaluated by preciseness, recall, flscore, accuracy, and AUC score. The results show that classifier LinearSVC victimization TF-IDE vectorization outperforms all different models with ninety-three accuracies.