Smart Drug Recommendation System for Healthcare Using ML Techniques

J. Raghunath; H. Touseen; G. Priyanka; G. Sumanth Reddy; G. Sireesha; T. Rajesh Reddy; M. Yogananda Reddy

doi:https://doi.org/10.47001/ICJES/2025.402006

Authors

J. Raghunath

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

H. Touseen

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

G. Priyanka

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

G. Sumanth Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

G. Sireesha

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

T. Rajesh Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

M. Yogananda Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

The increasing burden on the healthcare system—exacerbated during pandemics like COVID-19—has exposed the urgent need for intelligent clinical decision support tools. This project proposes a smart drug recommendation system using Machine Learning (ML) and Natural Language Processing (NLP). The system leverages sentiment analysis on patient reviews to determine drug efficacy and predict the best drug recommendations for various conditions. Through feature engineering and vectorization techniques like Bow, TF-IDF, and Word2Vec, multiple classifiers were trained and evaluated. Among them, the Linearis with TF-IDF vectorization achieved the highest accuracy of 93%. This model aids healthcare professionals in making data-driven prescription decisions.

Keywords

Sentiment Analysis Drug Recommendation Machine Learning NLP TF-IDF Word2Vec Healthcare AI

Citation of this Article

J. Raghunath, H. Touseen, G. Priyanka, G. Sumanth Reddy, G. Sireesha, T. Rajesh Reddy, & M. Yogananda Reddy. (2025). Smart Drug Recommendation System for Healthcare Using ML Techniques. International Current Journal of Engineering and Science - ICJES, 4(2), 31-36. Article DOI: https://doi.org/10.47001/ICJES/2025.402006

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[2] Wittich CM, Burkle CM, Lanier WL. Medication errors: an overview for clinicians. Mayo Clin Proc. 2014 Aug;89(8):1116-25.

[3] CHEN, M. R., & WANG, H. F. (2013). The reason and prevention of hospital medication errors. Practical Journal of Clinical Medicine, 4.

[4] Drug Review Dataset, https://archive.ics.uci.edu/ml/datasets/Drug% 2BReview%2BDataset%2B%2528Drugs.com%2529#

[5] Fox, Susannah, and Maeve Duggan.” Health online 2013. 2013.” URL: http://pewinternet.org/Reports/2013/Health-online.aspx

[6] Bartlett JG, Dowell SF, Mandell LA, File TM Jr, Musher DM, Fine MJ. Practice guidelines for the management of community-acquired pneumonia in adults. Infectious Diseases Society of America. Clin Infect Dis. 2000 Aug; 31(2):347-82. Doi: 10.1086/313954. Epub 2000 Sep 7. PMID: 10987697; PMCID: PMC7109923.

[7] Fox, Susannah & Duggan, Maeve. (2012). Health Online 2013. Pew Research Internet Project Report.

[8] T. N. Tekade and M. Emmanuel,” Probabilistic aspect mining approach for interpretation and evaluation of drug reviews,” 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi, 2016, pp. 1471- 1476, doi: 10.1109/SCOPES.2016.7955684.

[9] Doulaverakis, C., Nikolaidis, G., Kleontas, A. et al. Galen OWL: Ontology-based drug recommendations discovery. J Biomed Semant 3, 14 (2012). https://doi.org/10.1186/2r041-1480- 3-14.

[10] Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong, and Yanming Xie. 2016. Data-driven Automatic Treatment Regimen Development and Recommendation. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16). Association for Computing Machinery, New York, NY, USA, 1865–1874. DOI: https://doi.org/10.1145/2939672.2939866.

[11] V. Goel, A. K. Gupta and N. Kumar,” Sentiment Analysis of Multilingual Twitter Data using Natural Language Processing,” 2018 8th International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India, 2018, pp. 208-212, doi: 10.1109/CSNT.2018.8820254.

[12] Shimada K, Takada H, Matsuyama S, et al. Drug-recommendation system for patients with infectious diseases. AMIA Annu Symp Proc. 2005; 2005:1112.

[13] Y. Bao and X. Jiang,” An intelligent medicine recommender system framework,” 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, 2016, pp. 1383-1388, doi: 10.1109/ICIEA.2016.7603801.

[14] Zhang, Yin & Zhang, Dafang & Hassan, Mohammad & Alamri, Atif & Peng, Limei. (2014). CADRE: Cloud-Assisted Drug Recommendation Service for Online Pharmacies. Mobile Networks and Applications. 20. 348-355. 10.1007/s11036-014-0537-4.

[15] J. Li, H. Xu, X. He, J. Deng and X. Sun,” Tweet modeling with LSTM recurrent neural networks for hashtag recommendation,” 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 1570-1577, doi: 10.1109/IJCNN.2016.7727385.

[16] Zhang, Yin & Jin, Rong & Zhou, Zhi-Hua. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics. 1. 43-52. 10.1007/s13042-010-0001- 0.

[17] J. Ramos et al., “Using tf-idf to determine word relevance in document queries,” in Proceedings of the first instructional conference on machine learning, vol. 242, pp. 133–142, Piscataway, NJ, 2003.

[18] Yoav Goldberg and Omer Levy. Word2vec Explained: deriving Mikolov et al.’s negative- sampling word-embedding method, 2014; arXiv: 1402.3722.

[19] Danushka Bolle gala, Takanori Maehara and Kenichi Wakabayashi. Unsupervised Cross- Domain Word Representation Learning, 2015; arXiv: 1505.07184. [20] Text blob, https://textblob.readthedocs.io/en/dev/.

[20] Van der Maaten, Laurens & Hinton, Geoffrey. (2008). Viualizing data using t-SNE. Journal of Machine Learning Research. 9. 2579-2605.

[21] N. V. Chawla, K. W. Bowyer, L. O. Hall and W. P. Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique, 2011, Journal Of Artificial Intelligence Research, Volume 16, pages 321- 357, 2002; arXiv:1106.1813. DOI: 10.1613/jair.953.