Medicine Recommendation System Based on Symptoms Using Machine Learning

Anish R. Karlekar, Yash B. Harmalkar and Kishor R. Bhosale

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 972-981

Abstract: The problem of access to prompt medical attention still represents a critical issue for many areas in the world, particularly for remote or low-income regions, in which availability of a doctor is exceptionally low. In a country such as India, for instance, where there exists one doctor per 834 people, patients are more prone to rely on self-medication with the ensuing potential for erroneous cures, side effects, or serious medical consequences. In order to remedy this issue, a Medicine Recommendation System was designed; based on patient symptoms, it suggests optimal medicines as well as details regarding dosage, side effects, and other useful information. Two datasets were utilized throughout this project: a symptom-prognosis dataset, containing 4920 training instances relating 41 disease classes and 132 binary symptom features, and a merged pharmaceutical database of 100,000 instances corresponding to 30 classes of drugs. The six machine learning algorithms (Random Forest, Gradient Boosting Classifier, Support Vector Machine, Decision Tree, K-Nearest Neighbour, Naive Bayes) trained were all tested against the training dataset and compared. Among all algorithms considered, Random Forest and Gradient Boosting demonstrated the highest accuracy of 97.62%; both algorithms proved highly effective in pinpointing the most indicative symptoms, identifying high fever, fatigue, and vomiting as the three most diagnostically relevant symptoms, and providing a set of detailed medical recommendations for each user case.

Keywords: Medicine Recommendation; Symptom-Based Diagnosis; Random Forest; Gradient Boosting; Healthcare Machine Learning; Multi-class Classification; Pharmaceutical Database; Feature Importance (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1689

DOI: 10.32628/IJSRST26133220

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