Drug Recommendation Using Multilabel Classification with Decision Tree Based on Patient Complaints and Diagnoses

Abstrak

This study develops a drug recommendation system using multilabel classification with the Decision Tree algorithm based on patient complaint and diagnosis data from electronic medical records. The dataset consists of patient visit records from a community health center in Pangkajene and Kepulauan Regency and is transformed using multi-hot encoding. Model performance is evaluated under three dataset scenarios (N=500, N=800, and N=1000) using multilabel metrics, including Micro-F1, Samples-F1, Hamming Loss, Jaccard Similarity, Hit@5, Precision@K, and Recall@K. The best Decision Tree model achieved a Micro-F1 score of 0.292, Samples-F1 of 0.281, and Hit@5 of 0.690 on the N=1000 dataset scenario. Bootstrap validation with 1000 iterations indicates relatively stable performance, with narrow confidence intervals across evaluation metrics. These results show that the multilabel Decision Tree model is capable of capturing relationships between patient complaints, diagnoses, and drug therapies while maintaining an interpretable decision structure