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Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms
Mila Glavaški1, Andrej Preveden1,2, Đorđe Jakovljević3,4
1Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia.
Insights
This study used machine learning to identify four subtypes of hypertrophic cardiomyopathy (HCM) in adult patients. These findings offer new insights into the complex mechanisms underlying this inherited cardiac disease.
Area of Science:
- Cardiology
- Medical Informatics
- Genetics
Background:
- Hypertrophic cardiomyopathy (HCM) is a common inherited cardiac condition causing left ventricular hypertrophy.
- Understanding HCM subtypes and mechanisms is crucial for effective patient management.
Purpose of the Study:
- To identify distinct subtypes of nonobstructive hypertrophic cardiomyopathy using machine learning.
- To explore the underlying mechanisms of HCM through algorithmic analysis of clinical and laboratory data.
Main Methods:
- Machine learning algorithms, including clustering and classification, were applied to analyze data from 143 adult patients with nonobstructive HCM.
- Clustering identified optimal HCM subtypes, while classification models predicted the presence of specific HCM features.
Main Results:
- Four distinct clusters, representing potential HCM subtypes, were identified as optimal for the dataset.
- Predictive models were developed to identify HCM features based on genotypic and phenotypic information, revealing key feature subsets.
Conclusions:
- The study proposes four novel subtypes of hypertrophic cardiomyopathy based on machine learning analysis of phenotypic expression.
- Identifying feature subsets offers deeper insights into HCM mechanisms and aids in subtype characterization.
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.
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