Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
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 met...
Saved in:
Main Authors: | Nanyonga Aziida,, Sorayya Malek,, Firdaus Aziz,, Khairul Shafiq Ibrahim,, Sazzli Kasim, |
---|---|
Format: | Article |
Language: | English |
Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
|
Online Access: | http://journalarticle.ukm.my/16915/1/17.pdf http://journalarticle.ukm.my/16915/ https://www.ukm.my/jsm/malay_journals/jilid50bil3_2021/KandunganJilid50Bil3_2021.html |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualisation
by: Aziida, Nanyonga, et al.
Published: (2021) -
Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida
by: Nanyonga , Aziida
Published: (2019) -
Impact of multicomponent integrated care on mortality and hospitalization after acute coronary syndrome: a systematic review and meta-analysis
by: Hoo, Jia-Xin, et al.
Published: (2023) -
The effects of early educational intervention on patients with acute coronary syndrome (ACS): a review
by: Abu, Harlinna, et al.
Published: (2020) -
Acute coronary syndrome & ECG interpretation
by: Shalihin, Mohd Shaiful Ehsan
Published: (2021)