Chen, Y, Gue, Y, Calvert, P, Gupta, D, McDowell, G, Azariah, JL, Namboodiri, N, Bucci, T, Jabir, A, Tse, HF, Chao, T-F, Lip, GYH, Bahuleyan, CG, KERALA-AF Registry, and APHRS-AF Registry Investigators, (2024) Predicting stroke in Asian patients with atrial fibrillation using machine learning: A report from the KERALA-AF registry, with external validation in the APHRS-AF registry. Current Problems in Cardiology, 49 (4). ISSN 0146-2806

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Abstract

Atrial fibrillation (AF) is a significant risk factor for stroke. Based on the higher stroke associated with AF in the South Asian population, we constructed a one-year stroke prediction model using machine learning (ML) methods in KERALA-AF South Asian cohort. External validation was performed in the prospective APHRS-AF registry. We studied 2101 patients and 83 were to patients with stroke in KERALA-AF registry. The random forest showed the best predictive performance in the internal validation with receiver operator characteristic curve (AUC) and G-mean of 0.821 and 0.427, respectively. In the external validation, the light gradient boosting machine showed the best predictive performance with AUC and G-mean of 0.670 and 0.083, respectively. We report the first demonstration of ML's applicability in an Indian prospective cohort, although the more modest prediction on external validation in a separate multinational Asian registry suggests the need for ethnic-specific ML models.

Item Type:	Article
Uncontrolled Keywords:	Atrial fibrillation; Kerala; South Asia; Stroke, machine learning; 1102 Cardiorespiratory Medicine and Haematology; Cardiovascular System & Hematology
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RS Pharmacy and materia medica
Divisions:	Nursing & Allied Health Pharmacy & Biomolecular Sciences
Publisher:	Elsevier
SWORD Depositor:	A Symplectic
Date Deposited:	16 Feb 2024 11:57
Last Modified:	16 Feb 2024 12:00
DOI or ID number:	10.1016/j.cpcardiol.2024.102456
URI:	https://researchonline.ljmu.ac.uk/id/eprint/22638

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