Intelligent Drug Delivery Systems: A Machine Learning Approach to Personalized Medicine

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This study proposes a novel framework for personalized drug delivery by leveraging machine learning techniques. Using a dataset of 10,000 patient records, we developed and evaluated three ensemble models—XGBoost, LightGBM, and CatBoost—to predict optimal drug delivery parameters based on individual characteristics. The dataset includes diverse attributes such as demographics, medical conditions, treatment history, and clinical outcomes, providing a solid foundation for personalized medicine. We performed extensive data preprocessing and feature engineering, followed by the implementation and comparison of the three machine learning algorithms. Results indicated that XGBoost achieved the best overall performance (accuracy = 0.6386, F1 = 0.6275), while LightGBM attained the highest recall (0.6578). Model performance was assessed using multiple metrics—accuracy, precision, recall, and F1 score—with particular attention to convergence and learning curves. These findings suggest that machine learning can effectively capture complex patterns in patient data to support personalized drug delivery. While the current models yield promising results, they highlight opportunities for improvement through larger datasets and more advanced algorithms. This work contributes to the evolving field of precision medicine by offering a quantitative framework to optimize drug delivery based on individual characteristics.