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XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation

Shweta Paliwal1, Amit Kumar Mishra2,*, Ram Krishn Mishra3, Nishad Nawaz4, M. Senthilkumar5

1 MIET Meerut, Meerut, 250005, India
2 School of Computing, DIT University, Dehradun, 248009, India
3 Department of Computer Science, BITS Pilani, Dubai Campus, Dubai, 345055, United Arab Emirates
4 Department of Business Management, College of Business Administration, Kingdom University, Riffa, 40434, Kingdom of Bahrain
5 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India

* Corresponding Author: Amit Kumar Mishra. Email: email

Computers, Materials & Continua 2022, 72(3), 5345-5362.


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.


Cite This Article

S. Paliwal, A. Kumar Mishra, R. Krishn Mishra, N. Nawaz and M. Senthilkumar, "Xgbrs framework integrated with word2vec sentiment analysis for augmented drug recommendation," Computers, Materials & Continua, vol. 72, no.3, pp. 5345–5362, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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