# AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data

arXiv: https://arxiv.org/abs/2508.02625 · HTML: https://mlsanity.com/paper/2508.02625/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Benchmarking Automatic Machine Learning Frameworks](https://mlsanity.com/paper/1808.06492/) (2018)
- [GAMA: a General Automated Machine learning Assistant](https://mlsanity.com/paper/2007.04911/) (2020)
- [Automated Imbalanced Learning](https://mlsanity.com/paper/2211.00376/) (2022)
- [Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims](https://mlsanity.com/paper/2107.10495/) (2021)

## Abstract (arXiv)

Medical datasets are typically affected by issues such as missing values, class imbalance, a heterogeneous feature types, and a high number of features versus a relatively small number of samples, preventing machine learning models from obtaining proper results in classification and regression tasks. This paper introduces AutoML-Med, an Automated Machine Learning tool specifically designed to address these challenges, minimizing user intervention and identifying the optimal combination of preprocessing techniques and predictive models. AutoML-Med's architecture incorporates Latin Hypercube Sampling (LHS) for exploring preprocessing methods, trains models using selected metrics, and utilizes Partial Rank Correlation Coefficient (PRCC) for fine-tuned optimization of the most influential preprocessing steps. Experimental results demonstrate AutoML-Med's effectiveness in two different clinical settings, achieving higher balanced accuracy and sensitivity, which are crucial for identifying at-risk patients, compared to other state-of-the-art tools. AutoML-Med's ability to improve prediction results, especially in medical datasets with sparse data and class imbalance, highlights its potential to streamline Machine Learning applications in healthcare.