Random forest-based prediction of stroke outcome

Abstract We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with isch...

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Autores principales:	Carlos Fernandez-Lozano, Pablo Hervella, Virginia Mato-Abad, Manuel Rodríguez-Yáñez, Sonia Suárez-Garaboa, Iria López-Dequidt, Ana Estany-Gestal, Tomás Sobrino, Francisco Campos, José Castillo, Santiago Rodríguez-Yáñez, Ramón Iglesias-Rey
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ecffb34346142b8adea9bd5b4185d38
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spelling	oai:doaj.org-article:4ecffb34346142b8adea9bd5b4185d382021-12-02T15:54:49ZRandom forest-based prediction of stroke outcome10.1038/s41598-021-89434-72045-2322https://doaj.org/article/4ecffb34346142b8adea9bd5b4185d382021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89434-7https://doaj.org/toc/2045-2322Abstract We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.Carlos Fernandez-LozanoPablo HervellaVirginia Mato-AbadManuel Rodríguez-YáñezSonia Suárez-GaraboaIria López-DequidtAna Estany-GestalTomás SobrinoFrancisco CamposJosé CastilloSantiago Rodríguez-YáñezRamón Iglesias-ReyNature 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 Carlos Fernandez-Lozano Pablo Hervella Virginia Mato-Abad Manuel Rodríguez-Yáñez Sonia Suárez-Garaboa Iria López-Dequidt Ana Estany-Gestal Tomás Sobrino Francisco Campos José Castillo Santiago Rodríguez-Yáñez Ramón Iglesias-Rey Random forest-based prediction of stroke outcome
description	Abstract We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.
format	article
author	Carlos Fernandez-Lozano Pablo Hervella Virginia Mato-Abad Manuel Rodríguez-Yáñez Sonia Suárez-Garaboa Iria López-Dequidt Ana Estany-Gestal Tomás Sobrino Francisco Campos José Castillo Santiago Rodríguez-Yáñez Ramón Iglesias-Rey
author_facet	Carlos Fernandez-Lozano Pablo Hervella Virginia Mato-Abad Manuel Rodríguez-Yáñez Sonia Suárez-Garaboa Iria López-Dequidt Ana Estany-Gestal Tomás Sobrino Francisco Campos José Castillo Santiago Rodríguez-Yáñez Ramón Iglesias-Rey
author_sort	Carlos Fernandez-Lozano
title	Random forest-based prediction of stroke outcome
title_short	Random forest-based prediction of stroke outcome
title_full	Random forest-based prediction of stroke outcome
title_fullStr	Random forest-based prediction of stroke outcome
title_full_unstemmed	Random forest-based prediction of stroke outcome
title_sort	random forest-based prediction of stroke outcome
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4ecffb34346142b8adea9bd5b4185d38
work_keys_str_mv	AT carlosfernandezlozano randomforestbasedpredictionofstrokeoutcome AT pablohervella randomforestbasedpredictionofstrokeoutcome AT virginiamatoabad randomforestbasedpredictionofstrokeoutcome AT manuelrodriguezyanez randomforestbasedpredictionofstrokeoutcome AT soniasuarezgaraboa randomforestbasedpredictionofstrokeoutcome AT irialopezdequidt randomforestbasedpredictionofstrokeoutcome AT anaestanygestal randomforestbasedpredictionofstrokeoutcome AT tomassobrino randomforestbasedpredictionofstrokeoutcome AT franciscocampos randomforestbasedpredictionofstrokeoutcome AT josecastillo randomforestbasedpredictionofstrokeoutcome AT santiagorodriguezyanez randomforestbasedpredictionofstrokeoutcome AT ramoniglesiasrey randomforestbasedpredictionofstrokeoutcome
_version_	1718385446496501760

Random forest-based prediction of stroke outcome

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