Recommendation of Drugs and Its Substitute Medicines Also Purchasing Using Cosine Similarity Vector

Authors

  • K. Saranya  Assistant Professor, Information Technology, Kings Engineering College, Tamil Nadu, India
  • R. Jayashree  Information Technology, Kings Engineering College, Tamil Nadu, India
  • A. Ranjitha  Information Technology, Kings Engineering College, Tamil Nadu, India

DOI:

https://doi.org/10.32628/IJSRSET231036

Keywords:

Drug, Recommendation System, Decision Tree, K-Nearest Neighbour, Cosine Similarity Vector, Machine Learning

Abstract

In the current digital era, healthcare is one of the main focuses of the medical sector. A mistake with a patient's medication is one of the most dangerous medical errors that might risk the patient's life. It leads the healthcare sector to assist users in making more suitable and cost- effective health-related decisions. A machine learning-based drug recommendation system that takes into account the patient's reported symptoms or drugs is suggested by this study. The algorithm utilised is cosine similarity vector and data frames of medicine, and the system uses supervised learning techniques such as decision trees and K- nearest neighbours to propose the alternative drug and urges users to buy that specific drug.

References

  1. Fox, S.(2000). The online health care revolution: How the web helps Americans Take better care of themselves. A Pew Internet and Americans Life Product Online Report.
  2. Eysenbach, G., & Kohler, C. (2003). What is the prevalence of health-related searches on the World Wide Web? Qualitative and quantitative analysis of search engine queries on the internet. In AMIA annual symposium proceedings (Vol. 2003, p. 225). American Medical Informatics Association.
  3. Iyer, M. (2011). Medical errors in top 10 Killers: WHO. The Times of India.
  4. Basiri, M. E., Abdar, M., Cifci, M. A., Nemati, S., & Acharya, U. R. (2020). A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowledge- Based Systems, 198, 105949.
  5. N. Komal Kumar and D. Vigneswari. A Drug Recommendation System for Multi- disease in Health Care using Machine Learning.
  6. Doma, V., Singh, S., Arora, N., Ortiz, G., Saran, P. K., Chavarin, S., & Pirouz, M. (2020). Automated drug suggestion using machine learning. In Advances in Information and Communication: Proceedings of the 2020 Future of Information and Communication Conference (FICC), Volume 2 (pp. 571- 589). Springer International Publishing.
  7. 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
  8. Gazalba, I., & Reza, N. G. I. (2017, November). Comparative analysis of k- nearest neighbor and modified k-nearest neighbor algorithm for data classification. In 2017 2nd international conferences on information technology, information systems and electrical engineering (ICITISEE) (pp. 294-298). IEEE.
  9. A Literature Review on Medicine Recommender Systems Benjamin Stark, Constanze Knahl, Mert Aydin, Karim Elish. Department of Computer Science, Florida Polytechnic University, Lakeland, USA.
  10. Y. Chen, J. Xiong, W. Xu, and J. Zuo, “A novel online incremental and decremental learning algorithm based on variable support vector machine,” Cluster Comput., vol. 22, no. 3, pp. 7435–7445, May 2019, doi: 10.1007/s10586- 018-1772-4.
  11. Okfalisa, I. Gazalba, Mustakim, and N. G. I. Reza, “Comparative analysis of k- nearest neighbor and modified k-nearest neighbor algorithm for data classification,” in Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017, Feb. 2018, vol. 2018-January, pp. 294–298, doi: 10.1109/ICITISEE.2017.8285514.
  12. Chiang, W.H., & Ning, X.Drug recommendation toward safe polypharmacy (supplementary materials). bioRxiv, 2019.
  13. J. Jiang, Y. Chen, X. Meng, L. Wang, and K. Li, “A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process,” Phys. A Stat. Mech. its Appl., vol. 523, pp. 702–713, Jun. 2019, doi:10.1016/j.physa.2019.03.012.
  14. Chen, Y., Xiong, J., Xu, W., & Zuo, J. (2019). A novel online incremental and decremental learning algorithm based on variable support vector machine. Cluster Computing, 22, 7435-7445.
  15. 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.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-06-30

Issue

Volume 10, Issue 3, May-June-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Saranya, R. Jayashree, A. Ranjitha "Recommendation of Drugs and Its Substitute Medicines Also Purchasing Using Cosine Similarity Vector" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 3, pp.28-33, May-June-2023. Available at doi : https://doi.org/10.32628/IJSRSET231036