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

新的增强方法提高了孟加拉语疾病预测的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种新方法，通过使用保持结构的增强技术来改进孟加拉语的疾病预测。该方法在 85 种疾病的 725 个配置文件基准上进行了测试，显示出 XGBoost、LightGBM 和 CatBoost 等提升树模型在准确性方面有了显著提高。虽然 Bernoulli Naive Bayes 取得了高准确率，但在严格校正后其增益未能维持，这凸显了在基准准确率之外考虑模板接近度的重要性。 AI

影响引入了一种新颖的数据增强技术，可显著提高某些机器学习模型的疾病预测准确性。

排序理由详细介绍新方法论和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Sabah Nushra, Shovan Samanta Turzo, Moinul Hossain, Md Kishor Morol · 2026-11-02 05:00

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

arXiv:2610.41733v1 Announce Type: cross Abstract: Bangla symptom-based disease prediction is commonly evaluated on a small, imbalanced structured benchmark containing many profiles that differ by only one or two symptoms. We test whether train-only asymmetric Bernoulli perturbati…

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

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