Interpretable machine learning model using Extreme Gradient Boosting with Nonnegative Matrix Factorization improves the accuracy of arrhythmic risk prediction in Brugada Syndrome

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Interpretable machine learning model using Extreme Gradient Boosting with Nonnegative Matrix Factorization improves the accuracy of arrhythmic risk prediction in Brugada Syndrome

Session: Ion channel diseases: risk stratification and treatment

Topic: Ion Channel Disorders

Speaker: Professor G. Tse (Hong Kong, CN)

Event: ESC Congress 2023

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Professor Gary Tse

Hong Kong Metropolitan University, Hong Kong (China)

2 presentations

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Ion channel diseases: risk stratification and treatment

Speakers: Professor G. Tse, Mrs P. Peltenburg, Doctor R. Tochibora, Miss E. Rossetto, Doctor J. Isbister...

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ESC Congress 2023

25 August - 28 August 2023

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