Acute coronary syndrome prediction in emergency care: A machine learning approach
Recommended Citation
Emakhu J, Monplaisir L, Aguwa C, Arslanturk S, Masoud S, Nassereddine H, Hamam MS, and Miller JB. Acute coronary syndrome prediction in emergency care: A machine learning approach. Comput Methods Programs Biomed 2022; 225:107080.
Document Type
Article
Publication Date
10-1-2022
Publication Title
Computer methods and programs in biomedicine
Abstract
BACKGROUND AND OBJECTIVE: Clinical concern for acute coronary syndrome (ACS) is one of emergency medicine's most common patient encounters. This study aims to develop an ensemble learning-driven framework as a diagnostic support tool to prevent misdiagnosis.
METHODS: We obtained extensive clinical electronic health data on patient encounters with clinical concerns for ACS from a large urban emergency department (ED) between January 2017 and August 2020. We applied an analytical framework equipped with many well-developed algorithms to improve the data quality by addressing missing values, dimensionality reduction, and data imbalance. We trained ensemble learning algorithms to classify patients with ACS or non-ACS etiologies of their symptoms. We used performance evaluation metrics such as accuracy, sensitivity, precision, F1-score, and the area under the receiver operating characteristic (AUROC) to measure the model's performance.
RESULTS: The analysis included 31,228 patients, of whom 563 (1.8%) had ACS and 30,665 (98.2%) had alternative diagnoses. Eleven features, including systolic blood pressure, brain natriuretic peptide, chronic heart disease, coronary artery disease, creatinine, glucose, heart attack, heart rate, nephrotic syndrome, red cell distribution width, and troponin level, are reported as significantly contributing risk factors. The proposed framework successfully classifies these cohorts with sensitivity and AUROC as high as 86.3% and 93.3%. Our proposed model's accuracy, precision, specificity, Matthew's correlation coefficient, and F1-score were 85.7%, 86.3%, 93%, 80%, and 86.3%, respectively.
CONCLUSION: Our proposed framework can identify early patients with ACS through further refinement and validation.
PubMed ID
36037605
Volume
225
First Page
107080
Last Page
107080
