Abstract
Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.
Original language | English |
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Article number | 1566 |
Number of pages | 20 |
Journal | Life |
Volume | 12 |
Issue number | 10 |
Early online date | 9 Oct 2022 |
DOIs | |
Publication status | E-pub ahead of print - 9 Oct 2022 |
Bibliographical note
This article is an open access article distributed under the terms andconditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
Funder
The research was funded by the Autonomous Province of Vojvodina—Projects of importance for the development of scientific research activities (2021–2024) under the contract No. 142-451-2568/2021-01.Keywords
- Article
- hypertrophic cardiomyopathy
- machine learning
- disease subtypes
- disease mechanisms
- computational biomedical research