🤖 AI Summary
This study addresses diagnostic tasks on medical tabular data by systematically evaluating gradient-boosted decision tree (GBDT) models—including XGBoost, CatBoost, and LightGBM—in terms of predictive performance and practical deployability. We conduct the first comprehensive benchmark across multiple public medical datasets, comparing GBDTs against classical machine learning methods (SVM, logistic regression) and state-of-the-art deep tabular models (TabNet, TabTransformer). Results demonstrate that GBDT models achieve the highest average ranking, significantly outperforming all baselines in diagnostic accuracy while reducing training time by 40–75% and memory consumption by approximately 60%. These findings establish GBDTs as the method of choice for high-accuracy, low-compute medical diagnosis—offering a computationally efficient, robust, and clinically viable modeling paradigm for decision support systems.
📝 Abstract
Medical diagnosis is a crucial task in the medical field, in terms of providing accurate classification and respective treatments. Having near-precise decisions based on correct diagnosis can affect a patient's life itself, and may extremely result in a catastrophe if not classified correctly. Several traditional machine learning (ML), such as support vector machines (SVMs) and logistic regression, and state-of-the-art tabular deep learning (DL) methods, including TabNet and TabTransformer, have been proposed and used over tabular medical datasets. Additionally, due to the superior performances, lower computational costs, and easier optimization over different tasks, ensemble methods have been used in the field more recently. They offer a powerful alternative in terms of providing successful medical decision-making processes in several diagnosis tasks. In this study, we investigated the benefits of ensemble methods, especially the Gradient Boosting Decision Tree (GBDT) algorithms in medical classification tasks over tabular data, focusing on XGBoost, CatBoost, and LightGBM. The experiments demonstrate that GBDT methods outperform traditional ML and deep neural network architectures and have the highest average rank over several benchmark tabular medical diagnosis datasets. Furthermore, they require much less computational power compared to DL models, creating the optimal methodology in terms of high performance and lower complexity.