Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning

Thoracic most cancers are a prime problem in the clinical field. Unexpected occur-ring cannot be predicted earlier but if the strategy is fine-tuned properly then the prognosis of cancer is not a major issue. But the problem is how to find out the proper layout with all possible features. The sector...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Ezzat A. Mansour
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	Thoracic Cancer Random Committee forest Machine learning algorithm. Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/e3789a75c2fd4fb4b4f110b3d228b91e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e3789a75c2fd4fb4b4f110b3d228b91e
record_format	dspace
spelling	oai:doaj.org-article:e3789a75c2fd4fb4b4f110b3d228b91e2021-11-30T07:59:31ZPrognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning10.3991/ijoe.v17i12.275732626-8493https://doaj.org/article/e3789a75c2fd4fb4b4f110b3d228b91e2021-11-01T00:00:00Zhttps://www.online-journals.org/index.php/i-joe/article/view/27573https://doaj.org/toc/2626-8493Thoracic most cancers are a prime problem in the clinical field. Unexpected occur-ring cannot be predicted earlier but if the strategy is fine-tuned properly then the prognosis of cancer is not a major issue. But the problem is how to find out the proper layout with all possible features. The sector of Thoracic Surgery is offering a source of the dataset with all feasible attributes of thoracic cancer. All the features suggested by this medical sector were approved by the Consortium of Tuberculosis and Pulmonary Diseases. The random committee is a novel hybrid algorithm that utilizes the benefit of both random forests with committee concepts. Many random forests are created as the result of the iteration. But anyone can be created and the committee analyses and retains any one optimal solution. Brei man, the first researcher to propose the general concept of Radio Frequency following the same he proposed the famous and most popular forest RF algorithm. Ezzat A. MansourInternational Association of Online Engineering (IAOE)articleThoracic CancerRandom Committee forestMachine learning algorithm.Computer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Online and Biomedical Engineering, Vol 17, Iss 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Thoracic Cancer Random Committee forest Machine learning algorithm. Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Thoracic Cancer Random Committee forest Machine learning algorithm. Computer applications to medicine. Medical informatics R858-859.7 Ezzat A. Mansour Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning
description	Thoracic most cancers are a prime problem in the clinical field. Unexpected occur-ring cannot be predicted earlier but if the strategy is fine-tuned properly then the prognosis of cancer is not a major issue. But the problem is how to find out the proper layout with all possible features. The sector of Thoracic Surgery is offering a source of the dataset with all feasible attributes of thoracic cancer. All the features suggested by this medical sector were approved by the Consortium of Tuberculosis and Pulmonary Diseases. The random committee is a novel hybrid algorithm that utilizes the benefit of both random forests with committee concepts. Many random forests are created as the result of the iteration. But anyone can be created and the committee analyses and retains any one optimal solution. Brei man, the first researcher to propose the general concept of Radio Frequency following the same he proposed the famous and most popular forest RF algorithm.
format	article
author	Ezzat A. Mansour
author_facet	Ezzat A. Mansour
author_sort	Ezzat A. Mansour
title	Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning
title_short	Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning
title_full	Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning
title_fullStr	Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning
title_full_unstemmed	Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning
title_sort	prognosis of thoracic cancer using the bierman random committee machine learning
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/e3789a75c2fd4fb4b4f110b3d228b91e
work_keys_str_mv	AT ezzatamansour prognosisofthoraciccancerusingthebiermanrandomcommitteemachinelearning
_version_	1718406778092257280

Prognosis of Thoracic Cancer Using the Bierman Random Committee Machine Learning

Ejemplares similares