Drug Recommendation System Using Machine Learing

Abstract

Machine learning’s value has soared in diverse applications, driving automation in creative work. We present a medication recommendation system to lighten experts’ workload. Our method suggests medications effectively. COVID-19 has strained clinical resources, leading to scarcity of experts, equipment, and medications. People resort to self-medication without guidance, worsening the health situation. Bow, TF-IDF, Word2Vec, and Manual Feature are the tools we employ. for emotion prediction. By employing categorization algorithms, we analyse and select appropriate medications. Accuracy, accuracy, remembrance, f1score, and AUC score are all important metrics. evaluate predictive sentiments. Results favour LinearSVC with TF-IDF.Vectorization,works superior to every other model, with an accuracy rate of 93%.