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Sains Malaysiana 50(3)(2021):
753-768
http://doi.org/10.17576/jsm-2021-5003-17
Predicting 30-Day Mortality after an Acute Coronary Syndrome (ACS) using Machine Learning Methods for Feature Selection, Classification and Visualisation (Meramalkan Kematian 30 Hari selepas Sindrom Koronari Akut (ACS) menggunakan Kaedah Pembelajaran Mesin untuk Pemilihan Ciri, Pengelasan dan Pemvisualan)
NANYONGA
AZIIDA1, SORAYYA MALEK1*, FIRDAUS AZIZ1,
KHAIRUL SHAFIQ IBRAHIM2 & SAZZLI KASIM2
1Bioinformatics Division, Institute of
Biological Sciences, University of Malaya, 50603
Kuala Lumpur, Federal Territory, Malaysia
2Department of Cardiology, Faculty of
Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Jalan
Hospital, 47000 Sungai Buloh, Selangor Darul Ehsan, Malaysia
Received: 23 December 2019/Accepted: 26
August 2020
ABSTRACT
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.
Keywords: Acute coronary
syndrome; feature selection; hybrid model; machine learning; self-organising
maps
ABSTRAK
Gabungan hibrid
pemilihan ciri, pengelasan dan pemvisualan menggunakan kaedah pembelajaran
mesin (ML) mempunyai potensi untuk pemahaman yang lebih baik untuk ramalan
kematian pesakit bagi tempoh 30 hari dengan penyakit kardiovaskular menggunakan
data penduduk yang khusus. Mengenal pasti ciri-ciri kaedah pemilihan
dengan algoritma pengelas yang menghasilkan prestasi tinggi dalam kajian
kematian adalah penting dan tidak pernah dilaporkan sebelum ini. Ciri-ciri
kaedah pemilihan seperti ‘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)
dan ‘Logistic Regression’ (LR) telah digabungkan dengan algoritma bagi
pengelasan RF, SVM, LR dan EN bagi ramalan kematian bagi tempoh 30
hari. Model ML telah dibina menggunakan 302 pesakit dan 54 pemboleh ubah
input dari Pangkalan Data Penyakit Kardiovaskular Kebangsaan
Malaysia. Pengesahan terbaik model ML telah dijalankan dengan Trombolisis dalam Infarksi Miokardium (TIMI) menggunakan set data tambahan daripada 102
pesakit. Peta swaurus (SOM) telah digunakan untuk menggambarkan faktor
yang berkaitan dengan kematian selepas ACS. Prestasi model diukur
menggunakan kawasan di bawah lengkung (AUC) antara 0.48-0.80. Model
terbaik mencatatkan (AUC = 0.80) adalah gabungan hibrid RF cara kepentingan
berubah-ubah, pemilihan ke belakang berurutan dan pengelas RF menggunakan lima
peramal (umur, trigliserida, kreatinin, troponin dan jumlah
kolesterol). Model terbaik telah dibandingkan dengan TIMI menggunakan set
data tambahan yang menyebabkan model ML mengatasi TIMI (AUC = 0.75 vs AUC =
0.60). Penemuan daripada kajian ini akan digunakan sebagai asas untuk
membangunkan talian ML berdasarkan pengiraan pemarkahan risiko yang penduduk
tertentu.
Kata kunci: Model hibrid; pembelajaran mesin;
pemilihan ciri; peta swaurus sindrom koronari akut
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*Corresponding author; email: sorayya@um.edu.my
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