Drug Recommendation System Based on Symptoms
Arati Kale1, Anup Lohar2, Umar Shaikh3, Shrikant Gophane4
1Dr. Arati Kale, Assistant Professor, Department of Information Technology, Trinity College of Engineering and Research, Pune (Maharashtra), India
2Anup Lohar, Student, Department of Information Technology, Trinity College of Engineering and Research, Pune (Maharashtra), India
3Umar Shaikh, Student, Department of Information Technology, Trinity College of Engineering and Research, Pune (Maharashtra), India
4Shrikant Gophane, Student, Department of Information Technology, Trinity College of Engineering and Research, Pune (Maharashtra), India
Manuscript received on 07 November 2024 | First Revised Manuscript received on 14 November 2024 | Second Revised Manuscript received on 21 January 2025 | Manuscript Accepted on 15 February 2025 | Manuscript published on 28 February 2025 | PP: 1-4 | Volume-5 Issue-2, February 2025 | Retrieval Number: 100.1/ijapsr.A406005011224 | DOI: 10.54105/ijapsr.A4060.05020225
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The integration of digital health technologies has transformed patient care by enabling the development of intelligent systems that assist in medical decision-making. This paper introduces a Drug Recommendation System (DRS) designed to analyze user-inputted symptoms and recommend appropriate medications. Utilizing advanced Natural Language Processing (NLP) techniques, the system preprocesses and classifies textual symptom data, facilitating accurate drug suggestions. The implementation of machine learning algorithms, particularly the Multinomial Naive Bayes classifier, allows for the effective prediction of suitable medications based on historical symptom-drug associations. This research underscores the potential of DRS in enhancing clinical workflows by reducing the cognitive load on healthcare providers and improving patient safety through tailored medication recommendations. Furthermore, the system’s user-friendly interface ensures accessibility, empowering patients with knowledge about their conditions and potential treatments. By harnessing the power of data-driven insights, this study aims to contribute to the evolution of personalized healthcare solutions, thereby improving patient outcomes and satisfaction.
Keywords: Drug Recommendation System, Natural Language Processing (NLP), Machine Learning, Healthcare Technology, Symptom Analysis, Multinomial Naive Bayes, User Input Processing, Medication Safety, Personalized Medicine, Digital Health Innovations.
Scope of the Article: Pharmaceutical Biotechnology