Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study

Abstract Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-E...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Marta Vigier, Benjamin Vigier, Elisabeth Andritsch, Andreas R. Schwerdtfeger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/edee2d171ae24450a6c36316b4192424
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:edee2d171ae24450a6c36316b4192424
record_format	dspace
spelling	oai:doaj.org-article:edee2d171ae24450a6c36316b41924242021-11-21T12:25:03ZCancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study10.1038/s41598-021-01779-12045-2322https://doaj.org/article/edee2d171ae24450a6c36316b41924242021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01779-1https://doaj.org/toc/2045-2322Abstract Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.Marta VigierBenjamin VigierElisabeth AndritschAndreas R. SchwerdtfegerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marta Vigier Benjamin Vigier Elisabeth Andritsch Andreas R. Schwerdtfeger Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study
description	Abstract Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.
format	article
author	Marta Vigier Benjamin Vigier Elisabeth Andritsch Andreas R. Schwerdtfeger
author_facet	Marta Vigier Benjamin Vigier Elisabeth Andritsch Andreas R. Schwerdtfeger
author_sort	Marta Vigier
title	Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study
title_short	Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study
title_full	Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study
title_fullStr	Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study
title_full_unstemmed	Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study
title_sort	cancer classification using machine learning and hrv analysis: preliminary evidence from a pilot study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/edee2d171ae24450a6c36316b4192424
work_keys_str_mv	AT martavigier cancerclassificationusingmachinelearningandhrvanalysispreliminaryevidencefromapilotstudy AT benjaminvigier cancerclassificationusingmachinelearningandhrvanalysispreliminaryevidencefromapilotstudy AT elisabethandritsch cancerclassificationusingmachinelearningandhrvanalysispreliminaryevidencefromapilotstudy AT andreasrschwerdtfeger cancerclassificationusingmachinelearningandhrvanalysispreliminaryevidencefromapilotstudy
_version_	1718419039810748416

Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study

Ejemplares similares