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

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...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Sains Malaysiana
المؤلف الرئيسي: Aziida N.; Malek S.; Aziz F.; Ibrahim K.S.; Kasim S.
التنسيق: مقال
اللغة:English
منشور في: Penerbit Universiti Kebangsaan Malaysia 2021
الوصول للمادة أونلاين: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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