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

 

Diserahkan: 23 Disember 2019/Diterima: 26 Ogos 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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*Pengarang untuk surat-menyurat; email: sorayya@um.edu.my

 

     

 

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