Predicting 30-day mortality after an acute coronary syndrome (acs) using machine learning methods for feature selection, classification and visualisation

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Predicting 30-day mortality after an acute coronary syndrome (acs) using machine learning methods for feature selection, classification and visualisation

Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method...

書誌詳細
出版年:	Sains Malaysiana
第一著者:	Aziida N.; Malek S.; Aziz F.; Ibrahim K.S.; Kasim S.
フォーマット:	論文
言語:	English
出版事項:	Penerbit Universiti Kebangsaan Malaysia 2021
オンライン･アクセス:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104477518&doi=10.17576%2fjsm-2021-5003-17&partnerID=40&md5=4338016cc1c3456bba3741d8e1fcfbf0

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