Scatter plot among diagnoses and MACE (%).

<div><p>Background</p><p>Studies of cardiovascular disease risk prediction by machine learning algorithms often do not assess their ability to generalize to other populations and few of them include an analysis of the interpretability of individual predictions. This manuscrip...

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Main Author:	Gilson Yuuji Shimizu (19837946) (author)
Other Authors:	Michael Schrempf (19837949) (author), Elen Almeida Romão (4772397) (author), Stefanie Jauk (19837952) (author), Diether Kramer (19837955) (author), Peter P. Rainer (5961086) (author), José Abrão Cardeal da Costa (19837958) (author), João Mazzoncini de Azevedo-Marques (3737785) (author), Sandro Scarpelini (4320544) (author), Katia Mitiko Firmino Suzuki (19837961) (author), Hilton Vicente César (19837964) (author), Paulo Mazzoncini de Azevedo-Marques (9073344) (author)
Published:	2024
Subjects:	Cell Biology Cancer Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preto medical school evaluated regarding accuracy applied towards insights 882 ); accuracy 792 ); accuracy 859 &# 8211 782 &# 8211 778 &# 8211 704 &# 8211 support vector machine based risk prediction ribeir &# 227 xlink "> studies xlink "> among machine learning algorithms shapley values suggest rpms ), university random forest showed bidmc ), usa best predictive performance roc curve ). best generalization ability 000 mace cases local interpretability analyses interpretability </ p &# 227 xlink "> machine learning shapley values roc curve random forest predictive performance mace cases mace ). local interpretability interpretability analyses year risk good generalization 000 non retrospective cohort nearest neighbors naive bayes model reliability manuscript addresses layer perceptron final model decision tree consistent explanations cardiovascular diseases brazilian hospital balanced sample additional one 808 )) 717 )).
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Scatter plot among diagnoses and MACE (%).

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