Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

Abstract

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity in the Arabian Gulf. In this study, the in‐hospital mortality amongst patients admitted with ACS to Arabian Gulf hospitals is predicted using a comprehensive modelling framework that combines powerful machine‐learning methods such as support‐vector machine (SVM), Naïve Bayes (NB), artificial neural networks (NN), and decision trees (DT). The performance of the machine‐learning methods is compared with that of the performance of a commonly used statistical method, namely, logistic regression (LR). The study follows the current practise of computing mortality risk using risk scores such as the Global Registry of Acute Coronary Events (GRACE) score, which has not been validated for Arabian Gulf patients. Cardiac registry data of 7,000 patients from 65 hospitals located in Arabian Gulf countries are used for the study. This study is unique as it uses a contemporary data analytics framework. A k‐fold (k = 10) cross‐validation is utilized to generate training and validation samples from the GRACE dataset. The machine‐learning‐based predictive models often incur prejudgments for imbalanced training data patterns. To mitigate the data imbalance due to scarce observations for in‐hospital mortalities, we have utilized specialized methods such as random undersampling (RUS) and synthetic minority over sampling technique (SMOTE). A detailed simulation experimentation is carried out to build models with each of the five predictive methods (LR, NN, NB, SVM, and DT) for the each of the three datasets k‐fold subsamples generated. The predictive models are developed under three schemes of the k‐fold samples that include no data imbalance, RUS, and SMOTE. We have implemented an information fusion method rooted in computing weighted impact scores obtain for an individual medical history attributes from each of the predictive models simulated for a collective recommendation based on an impact score specific to a predictor. Finally, we grouped the predictors using fuzzy c‐mean clustering method into three categories, high‐, medium‐, and low‐risk factors for in‐hospital mortality due to ACS. Our study revealed that patients with medical history related to the presences of peripheral artery disease, congestive heart failure, cardiovascular transient ischemic attack valvular disease, and coronary artery bypass grafting amongst others have the most risk for in‐hospital mortality.

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

“…►Table 5 presents the number of demographics, clinical, and therapeutic features in the prediction model. In a study by Raza et al, 45 1-year mortality was predicted with 24 features, such as History of MI, hyperlipidemia, HR, SBP, DBP, diabetes mellitus, Killip class type, and ECG finding. The AUC achieved by LR model in this study was 0.843.…”

“…The extracted variables of the selected studies were divided into three categories, i.e., demographic, clinical, and therapeutic features. 45 one-year mortality was predicted with 24 features, such as History of MI, Hyperlipidemia, HR , SBP, DBP, Diabetes Mellitus, Killip class type, and ECG Finding. The AUC achieved by LR model in this study was 0.843.…”

“…The AUC achieved by LR model in this study was 0.843. 45 Killip class is an important independent predictor of mortality and higher types are associated with increased mortality risk in ACS patients. 46 crackles or rales in the lungs, class III is defined as patients with evident acute pulmonary edema, and class IV refers to patients with cardiogenic shock or hypotension.…”

“…As shown in ►Table 6 all studies highlighted the limits in non-ML approaches. However, during the model training process, it was found that two studies 35,45 lacked information about feature engineering methods, and one study 35 did not provide the program or the platform for model training. Moreover, hyper parameters, which are necessary for the training process, were not found in nine (4.9%) studies.…”

Endoscopy report mining for intelligent gastric cancer screening

“…In the past decade, with the rapid advances in data mining and ML as well as the extensive availability of medical data, CDSS has aroused great interest in industry and academia (Abidi, 2017;Raza et al, 2019;Wang et al, 2019;Xia et al, 2019). For example, IBM Watson for Oncology is one representative CDSS by integrating and analysing patients' electronic health records, medical literature, and expert experience to realize the automatic generation of malignant tumour treatment.…”