Optimizing Drug Recommendations Through Sentiment Analysis and Machine Learning Techniques

Authors

  • Dr. P. Hasitha Reddy, Manisha.S, B. Sushmitha, J. Vignesh, G. Vinay Teja Author

DOI:

https://doi.org/10.48047/

Keywords:

Drug recommendation, machine learning, multi-layer perception.

Abstract

The COVID-19 pandemic has exacerbated the inaccessibility of legitimate medical resources, leading to a critical shortage of specialists and healthcare professionals, inadequate supplies of essential equipment and medications, and increased mortality rates. In response to these challenges, many individuals have resorted to self-medication without proper medical consultation, further deteriorating 
their health conditions. Machine learning has emerged as a valuable tool in various applications, and its potential for automation has sparked significant interest in research and development. 

Downloads

Download data is not yet available.

References

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

K. Shimada, H. Takada, S. Mitsuyama, H. Ban, H. Matsuo, H. Otake, H. Kunishima, K. Kanemitsu and M. Kaku. “Drug-recommendation system for patients with infectious diseases”. AMIA Annu Symp Proc. 2005;2005:1112. PMID: 16779399; PMCID: PMC1560833.

H. He, Y. Bai, E. A. Garcia and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning”, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, pp. 1322-1328, doi: 10.1109/IJCNN.2008.4633969.

Downloads

Published

2021-01-05

Issue

Section

Articles

How to Cite

Dr. P. Hasitha Reddy, Manisha.S, B. Sushmitha, J. Vignesh, G. Vinay Teja. (2021). Optimizing Drug Recommendations Through Sentiment Analysis and Machine Learning Techniques . History of Medicine, 7(1), 117-132. https://doi.org/10.48047/