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• W4225529305 abstract "Machine Learning is revolutionizing the era day by day and the scope is no more limited to computer science as the advancements are evident in the field of healthcare. Disease diagnosis, personalized medicine, and Recommendation system (RS) are among the promising applications that are using Machine Learning (ML) at a higher level. A recommendation system helps inefficient decision-making and suggests personalized recommendations accordingly. Today people share their experiences through reviews and hence designing of recommendation system based on users’ sentiments is a challenge. The recommendation system has gained significant attention in different fields but considering healthcare, little is being done from the perspective of drugs, disease, and medical recommendations. This study is engrossed in designing a recommendation system that is based on the fusion of sentiment analysis and radiant boosting. The polarity of the sentiments is analyzed through user reviews and the processed data is fed into the Extreme Gradient Boosting (XGBOOST) framework to generate the drug recommendation. To establish the applicability of the concept a comparative study is performed between the proposed approach and the existing approaches." @default.
• W4225529305 created "2022-05-05" @default.
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• W4225529305 date "2022-01-01" @default.
• W4225529305 modified "2023-09-25" @default.
• W4225529305 title "XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation" @default.
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• W4225529305 doi "https://doi.org/10.32604/cmc.2022.025858" @default.
• W4225529305 hasPublicationYear "2022" @default.
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