Non-specific myocardial fibrosis in young competitive athletes, predicted by a powerful machine learning-based model
Authors: Androulakis E , Merghani A , Papatheodorou E , Sharma S , Papadakis M 
Published: May 2023

Funding Acknowledgements

Type of funding sources: None.

Background

Non-specific myocardial fibrosis (NSMF) is a heterogeneous entity with potentially significant clinical implications. We aimed to create a machine-learning (ML) based model for its prediction, based on thorough baseline investigations in a cohort of young competitive athletes with and without NSMF.

Methods

We analysed data from 328 young athletes referred to our Sports Cardiology service for a variety of reasons. All athletes underwent an evaluation with ECG, Holter, cardiopulmonary exercise test and cardiac magnetic resonance (CMR). We identified 61 athletes with NSMF (80% male, 72% white, 65% endurance sport) and compared them with a matched group of 75 athletes with no fibrosis (controls). Athletes with NSMF were further divided into Group 1 (n=28) with minimal (‘minor’=insertion point) fibrosis and Group 2 (n=33), (all other patterns, ‘major’) fibrosis. Selected baseline athletes’ characteristics including demographics, ECG findings , exercise intensity and type, arrhythmia, echo and CMR findings were tested to train the model. Finally, we tested various machine learning (ML) algorithms to create a model which classifies the individuals under observance into two distinct classes; Class A which contains the control group plus the athletes with ‘minor’ fibrosis, while the second, Class B which contains the athletes with ‘major’ fibrosis. We created 4 different classifiers; a logistic regression classifier, a random forest classifier, a naive Bayes classifier, and a voting classifier which chooses the class predicted by the majority of the previous three.

Results

Given that ~75% of athletes belonged to either no fibrosis or Group 1 (class A), and ~25% to Group 2, it can be inferred that a dummy classifier who would always predict class A would achieve an accuracy of 75%. We therefore used such a classifier method, which a-priori predicts class A, as a baseline to which we compared the results of our trained classifiers. We split the dataset, trained the model, and measured its accuracy (and the accuracy of the benchmark a-priori classifier) on the test set for each classifier. Machine learning classifiers had the following accuracy classification results;

ȃȃA-priori classifier: 75% [74%, 76]

ȃȃNaive Bayes: 83% [81%, 85%] &lt;0.001

ȃȃLogistic Regression: 87% [86%, 88%] &lt;0.001

ȃȃVoting classifier: 89% [88%, 90%] &lt;0.001

ȃȃRandom forest: 90% [89%, 91%] &lt;0.001

Testing the models derived by machine learning, all our models significantly outperformed the benchmark method and the best accuracy was achieved by the random forest classifier with 90% mean accuracy.

Conclusions

This study showed that ECG findings, demographics and comprehensive baseline assessment can be used to develop a powerful ML-based prediction model of NSMF. Testing ML-derived models, they all significantly outperformed the benchmark method and the best accuracy was achieved by the random forest classifier with 90% mean accuracy.