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A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Lookup NU author(s): Dr Guy MacGowanORCiD, Professor Djordje JakovljevicORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2021 The Author(s). Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.

Publication metadata

Author(s): Smole T, Zunkovic B, Piculin M, Kokalj E, Robnik-Sikonja M, Kukar M, Fotiadis DI, Pezoulas VC, Tachos NS, Barlocco F, Mazzarotto F, Popovic D, Maier L, Velicki L, MacGowan GA, Olivotto I, Filipovic N, Jakovljevic DG, Bosnic Z

Publication type: Article

Publication status: Published

Journal: Computers in Biology and Medicine

Year: 2021

Volume: 135

Print publication date: 01/08/2021

Online publication date: 12/07/2021

Acceptance date: 08/07/2021

Date deposited: 18/08/2023

ISSN (print): 0010-4825

ISSN (electronic): 1879-0534

Publisher: Elsevier Ltd


DOI: 10.1016/j.compbiomed.2021.104648

Data Access Statement: The data underlying this article cannot be shared publicly to ensure the privacy of patients from the Careggi University Hospital, Florence, Italy. The data will be shared on reasonable request to the corresponding author. The final obtained decision model will also be shared on reasonable request to the corresponding author.


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Funder referenceFunder name
777204Commission of the European Communities