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arXiv Machine Learning By Riad Hossain, Muhammad Ashad Kabir, Arat Ibne Golam Mowla, Animesh Chandra Roy, Ranjit Kumar Ghosh

BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech

Read the original on arXiv Machine Learning →

BenSparX introduces the first Bengali conversational speech dataset for Parkinson’s disease detection and pairs it with a robust, explainable machine learning framework. The framework uses diverse acoustic features, systematic feature selection, and advanced classifiers, achieving 95.67% accuracy, 95.62% F1, and 0.990 AUC. SHAP analysis is employed to explain feature contributions, and the model outperforms state‑of‑the‑art methods on other language datasets.

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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