AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data
This addresses the challenge of building effective ML models for healthcare applications with minimal expert intervention, though it appears incremental as an adaptation of AutoML to medical data.
The authors tackled the problem of applying machine learning to medical tabular data, which suffers from issues like missing values and class imbalance, by developing AutoML-Med, an automated framework that achieved higher balanced accuracy and sensitivity compared to state-of-the-art tools in clinical settings.
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.