A Reproducible Framework for Bias-Resistant Machine Learning on Small-Sample Neuroimaging Data

• URL: http://arxiv.org/abs/2602.02920v1
• Date: Mon, 02 Feb 2026 23:47:57 GMT
• Title: A Reproducible Framework for Bias-Resistant Machine Learning on Small-Sample Neuroimaging Data
• Authors: Jagan Mohan Reddy Dwarampudi, Jennifer L Purks, Joshua Wong, Renjie Hu, Tania Banerjee,
• Abstract summary: We introduce a reproducible, bias-resistant machine learning framework that integrates domain-informed feature engineering, nested cross-validation, and calibrated decision-threshold optimization for small-sample neuroimaging data.<n>By combining interpretability and unbiased evaluation, this work provides a generalizable computational blueprint for reliable machine learning in data-limited biomedical domains.
• Score: 1.2287733479434337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a reproducible, bias-resistant machine learning framework that integrates domain-informed feature engineering, nested cross-validation, and calibrated decision-threshold optimization for small-sample neuroimaging data. Conventional cross-validation frameworks that reuse the same folds for both model selection and performance estimation yield optimistically biased results, limiting reproducibility and generalization. Demonstrated on a high-dimensional structural MRI dataset of deep brain stimulation cognitive outcomes, the framework achieved a nested-CV balanced accuracy of 0.660\,$\pm$\,0.068 using a compact, interpretable subset selected via importance-guided ranking. By combining interpretability and unbiased evaluation, this work provides a generalizable computational blueprint for reliable machine learning in data-limited biomedical domains.

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