Predicting psoriasis using routine laboratory tests with random forest.

Psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. It has significant impacts on both physical and emotional health-related quality of life comparable to other major illnesses. Accurately prediction of psoriasis using biomarkers from routine lab...

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Autores principales:	Jing Zhou, Yuzhen Li, Xuan Guo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe78386cb6f0406d9268e8f434779fb3
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spelling	oai:doaj.org-article:fe78386cb6f0406d9268e8f434779fb32021-12-02T20:16:47ZPredicting psoriasis using routine laboratory tests with random forest.1932-620310.1371/journal.pone.0258768https://doaj.org/article/fe78386cb6f0406d9268e8f434779fb32021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258768https://doaj.org/toc/1932-6203Psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. It has significant impacts on both physical and emotional health-related quality of life comparable to other major illnesses. Accurately prediction of psoriasis using biomarkers from routine laboratory tests has important practical values. Our goal is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests. We collected a data set including 466 psoriasis patients and 520 healthy controls with 81 variables from only laboratory routine tests, such as age, total cholesterol, HDL cholesterol, blood pressure, albumin, and platelet distribution width. In this study, Boruta feature selection method was applied to select the most relevant features, with which a Random Forest model was constructed. The model was tested with 30 repetitions of 10-fold cross-validation. Our classification model yielded an average accuracy of 86.9%. 26 notable features were selected by Boruta, among which 15 features are confirmed from previous studies, and the rest are worth further investigations. The experimental results demonstrate that the machine learning approach has good potential in predictive modeling for the psoriasis disease given the information only from routine hospital tests.Jing ZhouYuzhen LiXuan GuoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258768 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jing Zhou Yuzhen Li Xuan Guo Predicting psoriasis using routine laboratory tests with random forest.
description	Psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. It has significant impacts on both physical and emotional health-related quality of life comparable to other major illnesses. Accurately prediction of psoriasis using biomarkers from routine laboratory tests has important practical values. Our goal is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests. We collected a data set including 466 psoriasis patients and 520 healthy controls with 81 variables from only laboratory routine tests, such as age, total cholesterol, HDL cholesterol, blood pressure, albumin, and platelet distribution width. In this study, Boruta feature selection method was applied to select the most relevant features, with which a Random Forest model was constructed. The model was tested with 30 repetitions of 10-fold cross-validation. Our classification model yielded an average accuracy of 86.9%. 26 notable features were selected by Boruta, among which 15 features are confirmed from previous studies, and the rest are worth further investigations. The experimental results demonstrate that the machine learning approach has good potential in predictive modeling for the psoriasis disease given the information only from routine hospital tests.
format	article
author	Jing Zhou Yuzhen Li Xuan Guo
author_facet	Jing Zhou Yuzhen Li Xuan Guo
author_sort	Jing Zhou
title	Predicting psoriasis using routine laboratory tests with random forest.
title_short	Predicting psoriasis using routine laboratory tests with random forest.
title_full	Predicting psoriasis using routine laboratory tests with random forest.
title_fullStr	Predicting psoriasis using routine laboratory tests with random forest.
title_full_unstemmed	Predicting psoriasis using routine laboratory tests with random forest.
title_sort	predicting psoriasis using routine laboratory tests with random forest.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fe78386cb6f0406d9268e8f434779fb3
work_keys_str_mv	AT jingzhou predictingpsoriasisusingroutinelaboratorytestswithrandomforest AT yuzhenli predictingpsoriasisusingroutinelaboratorytestswithrandomforest AT xuanguo predictingpsoriasisusingroutinelaboratorytestswithrandomforest
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Predicting psoriasis using routine laboratory tests with random forest.

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