Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.

This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the surviva...

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Autores principales:	Keyvan Karami, Mahboubeh Akbari, Mohammad-Taher Moradi, Bijan Soleymani, Hossein Fallahi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fd77825e3e4741b6aa5d9b0b2418aa8a
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spelling	oai:doaj.org-article:fd77825e3e4741b6aa5d9b0b2418aa8a2021-12-02T20:06:42ZSurvival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.1932-620310.1371/journal.pone.0254976https://doaj.org/article/fd77825e3e4741b6aa5d9b0b2418aa8a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254976https://doaj.org/toc/1932-6203This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.Keyvan KaramiMahboubeh AkbariMohammad-Taher MoradiBijan SoleymaniHossein FallahiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254976 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Keyvan Karami Mahboubeh Akbari Mohammad-Taher Moradi Bijan Soleymani Hossein Fallahi Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
description	This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.
format	article
author	Keyvan Karami Mahboubeh Akbari Mohammad-Taher Moradi Bijan Soleymani Hossein Fallahi
author_facet	Keyvan Karami Mahboubeh Akbari Mohammad-Taher Moradi Bijan Soleymani Hossein Fallahi
author_sort	Keyvan Karami
title	Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
title_short	Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
title_full	Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
title_fullStr	Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
title_full_unstemmed	Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
title_sort	survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fd77825e3e4741b6aa5d9b0b2418aa8a
work_keys_str_mv	AT keyvankarami survivalprognosticfactorsinpatientswithacutemyeloidleukemiausingmachinelearningtechniques AT mahboubehakbari survivalprognosticfactorsinpatientswithacutemyeloidleukemiausingmachinelearningtechniques AT mohammadtahermoradi survivalprognosticfactorsinpatientswithacutemyeloidleukemiausingmachinelearningtechniques AT bijansoleymani survivalprognosticfactorsinpatientswithacutemyeloidleukemiausingmachinelearningtechniques AT hosseinfallahi survivalprognosticfactorsinpatientswithacutemyeloidleukemiausingmachinelearningtechniques
_version_	1718375400602599424

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.

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