Comparative Explainable Machine Learning Framework for Multiclass Drug Classification

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Accurate drug classification is important for developing intelligent healthcare systems and supporting reliable medication-related decision-making.The relationships between patient characteristics and prescribed drug categories may be nonlinear and difficult to identify using conventional analytical procedures.This study presents a machine learning framework for multiclass drug classification using demographic and clinical patient attributes.The framework includes data cleaning, exploratory data analysis, numerical and categorical feature transformation, model training, comparative evaluation, and explainable artificial intelligence analysis.Nine classification models were investigated, including Random Forest, support vector machine, LightGBM, CatBoost, multilayer perceptron, one-dimensional convolutional neural network, long short-term memory network, gated recurrent unit network, and TabNet.The models were evaluated using accuracy, weighted precision, weighted recall, weighted F1-score, macro F1-score, and balanced accuracy.Random Forest achieved the best overall balance between predictive performance and interpretability, obtaining an accuracy of 97.50%, a macro F1-score of 98.51%, and a balanced accuracy of 98.18%.SHAP analysis identified the sodium-to-potassium ratio, blood-pressure categories, and age as the most influential variables affecting the classification decisions.The obtained results demonstrate that an explainable machine learning framework can provide accurate and transparent drug classification using a limited number of patient-related variables.