International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The abstract begins by highlighting how machine learning (ML) and artificial intelligence (AI) are transforming healthcare by creating intelligent applications that assist doctors in making better decisions. Among these applications is the Medicine Recommendation System (MRS), which is designed to suggest the most suitable medicine for a patient. Unlike traditional prescribing methods that rely mainly on a doctor’s experience, the MRS uses patient-specific information such as symptoms, medical history, and demographic details to make recommendations.

The research focuses on designing, implementing, and evaluating this system. To do so, it collects and processes different types of data, including electronic health records (EHRs), laboratory test results, and drug interaction databases. These inputs ensure that the system not only considers the patient’s current condition but also past illnesses and possible side effects that may arise from combining different drugs.

To analyze this information, several machine learning algorithms are applied. Decision trees and random forests are chosen for their interpretability and robustness; logistic regression is used as a simple baseline model; support vector machines (SVM) are tested for their strength in classification problems; and deep neural networks are explored for their ability to recognize complex patterns in medical data.

Once trained, the system is able to generate personalized medicine recommendations for new patients. This has significant benefits: it can improve prescription accuracy, minimize adverse drug reactions, and provide valuable support to healthcare professionals. Instead of replacing doctors, the system works as a decision-support tool, offering evidence-based suggestions that doctors can review and approve.

In conclusion, the abstract conveys that an ML-driven Medicine Recommendation System has the potential to make healthcare more reliable, safe, and tailored to individual patients, ultimately improving the overall quality of medical treatment.

Downloads: | DOI: 10.17148/IJARCCE.2025.141022