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

• Main Authors: Aziida, Nanyonga, Malek, Sorayya, Aziz, Firdaus, Ibrahim, Khairul Shafiq, Kasim, Sazzli
• Format: Article
• Published: Penerbit Universiti Kebangsaan Malaysia 2021
• Subjects: Q Science (General); QH301 Biology; R Medicine (General)
• Online Access: http://eprints.um.edu.my/28045/

Hybrid combinations of feature selection, classification and visualisation using machine learning (ham) 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...