A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
arXiv:2605.21528v1 Announce Type: new Abstract: Accurate and reproducible disease risk prediction remains challenging due to heterogeneous features, limited samples, and severe class imbalance. This study introduces yvsoucom-iterkit, a deterministic and log-driven automated machine learning framework that formulates pipeline optimization as a fully reproducible, configuration-level system. Each pipeline is encoded as a traceable log entity, enabling analysis of component attribution, interactions, similarity, and cross-seed robustness. Experiments on the Pima Indians Diabetes and Stroke datase
The increasing complexity of AI models and the critical need for reliable, interpretable predictions in sensitive fields like healthcare are driving the development of robust AutoML frameworks.
This development represents a step towards more trustworthy and deployable AI in healthcare, improving disease risk prediction and treatment pathways.
The ability to fully trace and reproduce AI pipeline optimization ensures greater accountability and interpretability, crucial for regulatory approval and clinical adoption.
- · Healthcare providers
- · Patients
- · AI/ML developers
- · Regulatory bodies
- · Black-box AI solutions
- · Developers of non-interpretable models
Improved accuracy and reliability in medical diagnostics and personalized medicine.
Faster adoption of AI in clinical settings due to increased trust and transparency.
New standards for AI ethics and reproducibility emerging as a key differentiator in healthcare technology.
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Read at arXiv cs.LG