A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
Authors: Rui Huang, Lican Huang
Summary
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.
Relevance
Read next because A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction overlaps with clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, stroke, class, line, rate, full, model. Source: arxiv cs.LG (Machine Learning).
Threat model
Potential threat/caveat for clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)": this item discusses robustness.
Abstract
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 datasets across more than 18,000 pipeline configurations reveal a structured and partially redundant search space, where performance is governed by a small subset of interacting components. Random Forest importance analysis identifies augmentation (0.454), model choice (0.198), and imbalance handling (0.101) as key drivers on Pima, while imbalance handling dominates Stroke (0.406). Component similarity analysis shows strong redundancy, with feature selection variants (biMax-biMean) exhibiting low RMS distance (0.0252), mixup closely matching no augmentation (0.0279), and TomekLinks aligning with no imbalance handling (0.0325), whereas Gaussian noise shows greater divergence from no augmentation (0.10). The framework achieves strong and stable performance using ensemble models (Weighted-F1 0.89, Macro-F1 0.88 on Pima; Weighted-F1 0.94 on Stroke), while Macro-F1 remains lower on Stroke (0.67) due to class imbalance. Cross-seed analysis reveals a performance-robustness trade-off, with ensembles showing lower variability (0.023-0.026) than SVM. These results indicate that effective AutoML optimization can focus on a reduced set of high-impact components.