A Comparative Study of Drug Prediction Models using KNN, SVM, and Random Forest

Accurate drug classification is essential in medical decision-making to ensure patients receive appropriate prescriptions based on their physiological and biochemical characteristics. This study compares the performance of K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest models in predicting drug prescriptions using patient attributes such as age, sex, blood pressure, cholesterol level, and sodium-to-potassium ratio. The dataset, obtained from Kaggle, was preprocessed and split into training and testing sets to evaluate model performance using accuracy as the primary metric. The results indicate that Random Forest outperformed KNN and SVM, achieving a perfect test accuracy of 100%, demonstrating superior generalization and robustness. SVM also performed well, with a test accuracy of 97.50%, while KNN achieved the lowest accuracy of 70%, indicating its limitations in handling complex feature interactions. These findings highlight the effectiveness of ensemble learning methods in medical classification tasks, suggesting that Random Forest is the most suitable model for drug prediction. Furthermore, the potential applications of these findings in clinical settings could enhance treatment outcomes and patient care. Future research should explore feature engineering techniques, larger datasets, and additional machine learning approaches to enhance predictive accuracy and applicability in real-world healthcare settings.