Dual explainability framework for heart disease prediction using LIME and permutation feature importance

Abstract Heart disease continues to be one of the leading causes of mortality worldwide, which highlights the immediate need for accurate and interpretable predictive models to support early detection. This work mainly focused a reasonable assessment of various effective machine learning (ML) Algori...

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Podrobná bibliografie

Hlavní autoři:	Zulfikar Ali Ansari, Wasim Khan, Md Shamsul Haque Ansari, Shahin Fatima, Shadab Siddiqui
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Springer 2025-12-01
Edice:	Discover Applied Sciences
Témata:	Heart Disease Machine Learning Explainable AI LIME
On-line přístup:	https://doi.org/10.1007/s42452-025-08108-5
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