Comparative Analysis of Machine Learning Models for Medical Drug Classification

Medical drug classification is very much needed, and designing a personalized health care system where treatment decisions are given based on individual patient conditions considering different characteristics. In this article, the proposal of a machine learning approach for medical drug classification is based on critical features, which include age, gender, blood pressure (BP), level of cholesterol and the natriuretic peptide (Na-to-k) ratio. Using a diverse dataset which has patient records also includes demographic information and clinical capacities. We have used advanced machine learning algorithms to predict the most suitable drug for a patient. Feature engineering techniques were applied to extract most related features from the considered dataset, ensuring extensive coverage of patient capacities. An enormous list of machine learning models are trained and evaluated to identify the optimal reach for drug classification. Ensemble techniques are considered to enhance prediction accuracy and robustness. Our results demonstrate the effectiveness of the proposed method in accurately classifying medical drugs based on individual patient profiles. By taking into account specific characteristics, the model achieves observable change in terms of performance in drug classification, facilitating personalized treatment recommendations in terms of improving health care delivery. The future could be fine-tuning or taking hybrid models with larger datasets and additional clinical parameters for enhanced prediction accuracy.