Abstract :

The Increasing Availability Of Online Drug Reviews Provides Valuable Insights Into Patient Experiences, Making It Possible To Analyze Sentiments And Recommend Effective Medications. However, Manually Analyzing Large Volumes Of Reviews Is Time-consuming And Inefficient. This Project Proposes A Drug Recommendation System Based On Sentiment Analysis Of Drug Reviews Using Machine Learning Techniques. The System Aims To Analyze User Feedback, Classify Sentiments, And Recommend Suitable Drugs Based On Positive Outcomes. The Proposed System Utilizes A Dataset Containing Drug Names, Medical Conditions, User Reviews, And Ratings. Preprocessing Techniques Such As Text Cleaning, Tokenization, Stop-word Removal, And Vectorization (TF-IDF) Are Applied To Convert Textual Data Into Numerical Form. Machine Learning Algorithms Such As Logistic Regression, Naïve Bayes, And Support Vector Machines Are Used To Classify Sentiments As Positive, Negative, Or Neutral. Based On The Sentiment Classification And Ratings, The System Recommends The Most Effective Drugs For Specific Conditions. The Performance Of The Models Is Evaluated Using Metrics Such As Accuracy, Precision, Recall, And F1-score. Experimental Results Demonstrate That The System Achieves High Accuracy In Sentiment Classification And Provides Reliable Drug Recommendations. This Approach Helps Patients And Healthcare Providers Make Informed Decisions By Leveraging Real-world Experiences, Improving Treatment Outcomes And Reducing Risks Associated With Medication Selection.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1972-1978

Section:

Articles

License:

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

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