An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data.

Kokkotis, Christos; Giarmatzis, Georgios; Giannakou, Erasmia; Moustakidis, Serafeim; Tsatalas, Themistoklis; Tsiptsios, Dimitrios; Vadikolias, Konstantinos; Aggelousis, Nikolaos

Kokkotis, Christos; Giarmatzis, Georgios; Giannakou, Erasmia; Moustakidis, Serafeim; Tsatalas, Themistoklis; Tsiptsios, Dimitrios; Vadikolias, Konstantinos; Aggelousis, Nikolaos.

Afiliação

Kokkotis C; Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.
Giarmatzis G; Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.
Giannakou E; Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.
Moustakidis S; AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia.
Tsatalas T; Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece.
Tsiptsios D; Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Vadikolias K; Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Aggelousis N; Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.

Diagnostics (Basel) ; 12(10)2022 Oct 01.

Article em En | MEDLINE | ID: mdl-36292081

Palavras-chave

clinical data; interpretation; machine learning; prognosis; stroke

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Grécia

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