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New augmentation method boosts Bangla disease prediction accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for improving disease prediction in Bangla by using structure-preserving augmentation techniques. This approach, tested on a benchmark of 725 profiles across 85 diseases, showed significant accuracy gains for boosted-tree models like XGBoost, LightGBM, and CatBoost. While Bernoulli Naive Bayes achieved high accuracy, its gains did not hold up under rigorous correction, highlighting the importance of considering template proximity alongside benchmark accuracy. AI

IMPACT Introduces a novel data augmentation technique that significantly improves disease prediction accuracy for certain machine learning models.

RANK_REASON Academic paper detailing a new methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New augmentation method boosts Bangla disease prediction accuracy

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Academic paper detailing a new methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · 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

    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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