Key result
A machine learning approach using anthropometric and pathological features yielded high accuracy in predicting endothelial dysfunction.
Why the study?
Does a machine learning-based approach accurately predict endothelial dysfunction using anthropometric and pathological characteristics?
Does a machine learning-based approach accurately predict endothelial dysfunction using anthropometric and pathological characteristics?
A machine learning approach utilizing anthropometric and pathological features can accurately predict endothelial dysfunction.
Answers are grounded in this paper and cite their sources.
Hypothesis-generating for ML endothelial dysfunction screening; leaves open prospective validation before clinical use.
Endothelial Dysfunction is achieving increasing importance, because it is strictly related to cardiovascular risks and it provides important prognostic data in addition to the classical ones. This paper introduces a machine learning approach for predicting Endothelial Dysfunction. The approach was applied and tested on a newly collected dataset, “Endothelial Dysfunction Dataset (EDD)” and several machine learning algorithms are compared. This method comprises features related to the anthropometric or pathological characteristics of the analysed subjects. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.
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Calamanti et al. (2018) studied Endothelial Dysfunction. Machine learning approach vs. Other machine learning algorithms was evaluated on Prediction of Endothelial Dysfunction. A machine learning approach using anthropometric and pathological features yielded high accuracy in predicting endothelial dysfunction.
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