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arXiv Machine Learning

Leakage-Controlled Structure-Preserving Augmentation and Template-Aware Evaluation for Bangla Symptom-Based Disease Prediction

The study evaluates a leakage‑controlled, structure‑preserving augmentation method—train‑only asymmetric Bernoulli perturbation—on Bangla symptom‑based disease prediction. Using 725 unique profiles across 85 diseases and 172 binary symptoms, the authors compare this augmentation against duplication, oversampling, class weighting, and SMOTEN, finding that boosted‑tree models (XGBoost, LightGBM, CatBoost) gain substantial accuracy improvements while Bernoulli Naive Bayes shows limited benefit after statistical correction. A Hamming‑distance audit reveals that most held‑out profiles are very close to training data, and robustness tests confirm that XGBoost and LightGBM maintain gains even when one‑bit disease profiles are purged.

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arXiv Computation and Language
Oct 22

Is a 9B Decision Foundation Model Needed for Symptom-to-Disease Classification? Fine-Tuning Clef-Flash Against TF-IDF Baselines

The paper evaluates whether a 9‑billion parameter decision foundation model, Clef‑Flash, is necessary for symptom‑to‑disease classification. Fine‑tuning the model on the 24‑class Symptom2Disease benchmark yields 95.42% accuracy, but simple TF‑IDF linear classifiers achieve higher accuracy (98.75%) with far faster inference. Ablation studies show that predictions can hinge on single descriptive clauses, indicating that the benchmark may not fully capture the model’s capabilities.

By S. M. Mahbub Uddin Ahmed
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arXiv Machine Learning
1d ago

Leakage- and Label-Aware Benchmarking of Class-Imbalance Strategies for Multimodal Chest X-Ray and EHR Classification

arXiv:2611.07950v1 Announce Type: new Abstract: Benchmarks of class-imbalance strategies for multimodal chest X-ray (CXR) and electronic health record (EHR) prediction are often evaluated on record-l...

By Dang-Khoa Bui, Quang-Huy Tran, Minh-Khoi Nguyen-Bui, Duc-Tuan Ngo, Thanh-Trong Tran, Tuan-Khoi Nguyen, Hoang-Anh Ngo
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arXiv AI
Sep 3

The Ceiling Is in the Channel: Auditing Learner Gaps and Measurement Frontiers in Clinical Prediction

The paper introduces a framework that distinguishes two causes of saturation in clinical prediction: a learner gap, where the model fails to use available information, and a measurement‑channel ceiling, where the recorded variables limit performance. It provides theoretical characterizations, finite‑sample diagnostics, and empirical audits across three large cohorts, showing that well‑tuned models approach the frontier while deficient learners leave large gaps. A PRISMA‑guided synthesis across 104 tasks reveals consistent channel‑level patterns, suggesting that improving the learner or the measurement channel can audit and potentially lift performance.

By Sayeed Shafayet Chowdhury, Nusrat Jahan, Snehasis Mukhopadhyay, Shiaofen Fang, Vijay R. Ramakrishnan
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arXiv Machine Learning
Sep 10

Prevalence calibration as shortcut mitigation

Shortcut learning occurs when classifiers rely on spurious correlations instead of true diagnostic features. The authors argue that this is essentially a calibration issue: unconstrained training aligns shortcut groups with their prevalence, causing over‑confidence in one group and under‑confidence in another. They introduce two encoder‑agnostic methods—a regularizer and a post‑hoc recalibration—to equalize prevalence across shortcut groups, achieving significant performance gains on chest‑drain‑pneumothorax benchmarks with both fine‑tuned CNNs and frozen foundation‑model backbones.

By Mohamed Amine Kina, Eike Petersen
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arXiv Machine Learning
Sep 14

Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective

The study introduces Smooth Net Benefit (σNB), a differentiable approximation of Net Benefit, as a training objective aimed at aligning predictive models with threshold‑specific clinical decisions. Experiments on the Framingham cardiovascular risk dataset and 44 TabZilla datasets show that σNB training yields modest improvements for logistic regression but little to no benefit for more flexible models such as GAMs and XGBoost. The authors conclude that σNB is not a universal replacement for negative log‑likelihood training, though it may be worth exploring in contexts where model flexibility is limited.

By Koen M. F. Gorgels, Lasai Barre\~nada, Maarten van Smeden, Ben Van Calster, Ewout W. Steyerberg, Wouter A. C. van Amsterdam
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