DP-FDT: The Drug Pair-Based Fuzzy Decision Tree for Accurate Drug Combination Prediction

This paper presents a novel algorithm, the Drug Pair-based Fuzzy Decision Tree (DP-FDT), designed to improve classification performance on imbalanced drug combination datasets. Unlike traditional decision trees, the DP-FDT integrates drug combination feature pairs into its feature selection and node construction processes to better capture the underlying relationships between features from different drugs. The algorithm employs a fuzzy splitting rule that allows samples to be assigned to both child nodes with certain probabilities, thereby enhancing the model’s fault tolerance. In addition, logistic regression classifiers are incorporated at impure leaf nodes to provide an additional means of separating samples that cannot be perfectly partitioned. Experimental results on two cell line datasets (A375 and A549) demonstrate that the DP-FDT achieves superior performance, particularly in terms of AUC and F1 score, compared with several state-of-the-art oblique decision trees and a deep learning baseline. It also improves recall for minority classes. Ablation studies confirm the positive contributions of its key components. Furthermore, a preliminary bioinformatics analysis provides insights into the biological processes associated with the most influential features identified by the model. Overall, the DP-FDT offers an effective and interpretable approach for analyzing complex drug combination data.