Drug Recommendation System Using TF-IDF Vectorization and Cosine Similarity
B. R. Poorna
Assistant Professor, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
Edwin Charles Mathew
Student, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
Abstract:
This paper presents a machine learning-based drug recommendation system implemented as a Python web application using Flask. The system enables users to input symptoms and receive a list of recommended drugs based on historical patient data. The project leverages natural language processing (NLP) techniques, specifically TF-IDF vectorization and cosine similarity, to match user-provided symptoms with reviews of drugs used for similar conditions. The system was developed to assist in the efficient selection of drugs, improve patient care, and demonstrate the application of machine learning in a real-world healthcare scenario. This paper details the project’s development, including its background, methodology, implementation, results, and potential future improvements.
Published in: International Journal of Research in Engineering, Science and Management (Volume 7, Issue 10, October 2024)
Page(s): 1-3
Date of Publication: 06/10/2024
Publisher: IJRESM
Cite as:
B. R. Poorna, Edwin Charles Mathew, “Drug Recommendation System Using TF-IDF Vectorization and Cosine Similarity,” in International Journal of Research in Engineering, Science and Management, vol. 7, no. 10, pp. 1-3, October 2024.