Leveraging Support Vector Machine-Driven Predictive Analytics for Personalized Medication Recommendation and Risk Mitigation in Clinical Decision Support Systems

Leveraging Support Vector Machine-Driven Predictive Analytics for Personalized Medication Recommendation and Risk Mitigation in Clinical Decision Support Systems

Authors- Janarthana Rajan M, Dr. V. Sumalatha

Abstract--Medication mistakes and inadaptable prescriptions also pose serious risks to patient safety, usually brought about by human decision-making and the intricateness of assessing varied medical data. To confront this issue, we suggest that a Medicine Recommendation System based on Support Vector Machine (SVM) be employed to help doctors choose the best medications according to unique patient profiles. The aim is to improve the accuracy of treatments using critical patient information, such as medical history, symptoms, drug interactions, allergies, and diagnostics. Advanced data preprocessing with feature extraction is followed by predictive modeling using SVM for personalized recommendation. Integration into existing hospital management systems is transparent, making the system easy to adopt into clinical practice. In addition, the system offers explainable recommendations and patient education regarding dosage and side effects and supports safe drug use. This smart solution enhances health outcomes, mitigates adverse drug reactions, lowers the number of prescription errors, and facilitates a more personalized, transparent, and effective healthcare experience.

DOI: /10.61463/ijset.vol.13.issue3.141