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: | , , , , |
|---|---|
| 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!
|
| Summary: | 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 with a classifier algorithm that produces high
performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta,
Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic
Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined
with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients
and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model
was performed against Thrombolysis in Myocardial Infarction (TIMI) using an additional dataset of 102 patients. The
Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post-ACS. The performance of ML
models using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a
hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using
five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with TIMI using an additional
dataset resulted in the best ML model outperforming the TIMI score (AUC = 0.75 vs. AUC = 0.60). The findings of this
study will provide a basis for developing an online ML-based population-specific risk scoring calculator. |
|---|