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arXiv Machine Learning By Sabah Nushra, Shovan Samanta Turzo, Moinul Hossain, Md Kishor Morol

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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