XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Artificial Intelligence in Healthcare · Text and Document Classification Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Healthcare 4.010.1109/EMR.2019.2930702 ↗
2. Healthcare 4.0: Trends, challenges and research directions10.1080/09537287.2019.1702226 ↗
3. A hybrid recommendation system considering visual information for predicting favorite restaurants10.1007/s11280-017-0437-1 ↗
4. Survey on collaborative filtering, content-based filtering and hybrid recommendation system10.5120/19308-0760 ↗
5. Utilizing learner’s negative ratings in semantic content-based recommender system for e-learning forum
6. Ontology and context based recommendation system using Neuro-Fuzzy classification10.1016/j.compeleceng.2018.01.034 ↗
7. What and where: A context-based recommendation system for object insertion10.1007/s41095-020-0158-8 ↗
8. A novel recommendation system based on semantics and context awareness10.1007/s00607-018-0627-4 ↗
9. Healthcare information systems: data mining methods in the creation of a clinical recommender system10.1080/17517575.2010.541287 ↗
10. Hybrid context aware recommendation system for E-Health care by merkle hash tree from cloud using evolutionary algorithm10.1007/s00500-019-04322-7 ↗
11. Improvising the performance of image-based recommendation system using convolution neural networks and deep learning10.1007/s00500-020-04803-0 ↗
12. HealthRecSys: A semantic content-based recommender system to complement health videos
13. A disease diagnosis and treatment recommendation system based on big data mining and cloud computing10.1016/j.ins.2018.01.001 ↗
14. An intelligent healthcare monitoring framework using wearable sensors and social networking data10.1016/j.future.2020.07.047 ↗
15. Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system10.1007/s11042-017-5515-y ↗
16. Type-2 fuzzy ontology-aided recommendation systems for IoT-based healthcare10.1016/j.comcom.2017.10.005 ↗
17. Recurrent neural network based recommendation for time heterogeneous feedback10.1016/j.knosys.2016.06.028 ↗
18. A healthcare monitoring system using random forest and internet of things (IoT)10.1007/s11042-019-7327-8 ↗
19. A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection10.1016/j.eswa.2011.09.061 ↗
20. DeepReco: Deep learning based health recommender system using collaborative filtering10.3390/computation7020025 ↗
21. A recommender system to quit smoking with mobile motivational messages: Study protocol for a randomized controlled trial
22. A privacy-aware deep learning framework for health recommendation system on analysis of big data
23. Recommendation system using feature extraction and pattern recognition in clinical care systems10.1080/17517575.2018.1557256 ↗
24. Big data analytics of social network marketing and personalized recommendations10.1007/s13278-021-00729-z ↗
25. Toward improving the prediction accuracy of product recommendation system using extreme gradient boosting and encoding approaches10.3390/sym12091566 ↗
26. SentiXGboost: Enhanced sentiment analysis in social media posts with ensemble XGBoost classifier10.1080/02533839.2021.1933598 ↗
27. An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews10.1016/j.asoc.2020.106935 ↗
28. A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning
29. A knowledge-based recommendation system that includes sentiment analysis and deep learning10.1109/TII.2018.2867174 ↗
30. A social recommender system by combining social network and sentiment similarity: A case study of healthcare10.1177/0165551516657712 ↗
31. A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce10.1016/j.asoc.2021.107396 ↗
32. A decision tree logic based recommendation system to select software fault prediction techniques10.1007/s00607-016-0489-6 ↗
33. Drugs rating generation and recommendation from sentiment analysis of drug reviews using machine learning
34. Drug recommendation system based on sentiment analysis of drug reviews using machine learning
35. Ambient healthcare approach with hybrid whale optimization algorithm and naive bayes classifier10.3390/s21134579 ↗