Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7

Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from myeloid cell derivatives that influences the results of the identification of AMLs, which includes myeloblast, monoblast, and megakaryoblast. Furthermore, they are divided into more specific types, including myeloblast...

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Autores principales:	Prakisya Nurcahya Pradana Taufik, Liantoni Febri, Hatta Puspanda, Aristyagama Yusfia Hafid, Setiawan Andika
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	acute myeloid leukemia classification k-nearest neighbor white blood cells Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/dff44182cbd54bc3b70102fece130250
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spelling	oai:doaj.org-article:dff44182cbd54bc3b70102fece1302502021-12-05T14:10:46ZUtilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M72391-543910.1515/eng-2021-0065https://doaj.org/article/dff44182cbd54bc3b70102fece1302502021-05-01T00:00:00Zhttps://doi.org/10.1515/eng-2021-0065https://doaj.org/toc/2391-5439Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from myeloid cell derivatives that influences the results of the identification of AMLs, which includes myeloblast, monoblast, and megakaryoblast. Furthermore, they are divided into more specific types, including myeloblasts, promyelocytes, monoblasts, promonocytes, monocytes, and megakaryoblasts, which must be clearly identified in order to further calculate the ratio value in the blood. Therefore, this research aims to classify these cell types using the K-nearest neighbor (KNN) algorithm. Three distance metrics are tested, namely, Euclidean, Chebychev, and Minkowski, and both the weighted and unweighted were tested. The features used as parameters are area, nucleus ratio, circularity, perimeter, mean, and standard deviation, and about 1,450 objects are used as training and testing data. In addition, to ensure that the classification is not overfitting, K-fold cross validation was conducted. The results show that the unweighted Minkowski distance acquired about 240 of 290 objects at K = 19, which is the best. Therefore, the unweighted Minkowski distance is selected for further analysis. The accuracy, recall, and precision values of KNN with unweighted Minkowski distance obtained from fivefold cross validation are 80.552, 44.145, and 42.592%, respectively.Prakisya Nurcahya Pradana TaufikLiantoni FebriHatta PuspandaAristyagama Yusfia HafidSetiawan AndikaDe Gruyterarticleacute myeloid leukemiaclassificationk-nearest neighborwhite blood cellsEngineering (General). Civil engineering (General)TA1-2040ENOpen Engineering, Vol 11, Iss 1, Pp 662-668 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acute myeloid leukemia classification k-nearest neighbor white blood cells Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	acute myeloid leukemia classification k-nearest neighbor white blood cells Engineering (General). Civil engineering (General) TA1-2040 Prakisya Nurcahya Pradana Taufik Liantoni Febri Hatta Puspanda Aristyagama Yusfia Hafid Setiawan Andika Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
description	Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from myeloid cell derivatives that influences the results of the identification of AMLs, which includes myeloblast, monoblast, and megakaryoblast. Furthermore, they are divided into more specific types, including myeloblasts, promyelocytes, monoblasts, promonocytes, monocytes, and megakaryoblasts, which must be clearly identified in order to further calculate the ratio value in the blood. Therefore, this research aims to classify these cell types using the K-nearest neighbor (KNN) algorithm. Three distance metrics are tested, namely, Euclidean, Chebychev, and Minkowski, and both the weighted and unweighted were tested. The features used as parameters are area, nucleus ratio, circularity, perimeter, mean, and standard deviation, and about 1,450 objects are used as training and testing data. In addition, to ensure that the classification is not overfitting, K-fold cross validation was conducted. The results show that the unweighted Minkowski distance acquired about 240 of 290 objects at K = 19, which is the best. Therefore, the unweighted Minkowski distance is selected for further analysis. The accuracy, recall, and precision values of KNN with unweighted Minkowski distance obtained from fivefold cross validation are 80.552, 44.145, and 42.592%, respectively.
format	article
author	Prakisya Nurcahya Pradana Taufik Liantoni Febri Hatta Puspanda Aristyagama Yusfia Hafid Setiawan Andika
author_facet	Prakisya Nurcahya Pradana Taufik Liantoni Febri Hatta Puspanda Aristyagama Yusfia Hafid Setiawan Andika
author_sort	Prakisya Nurcahya Pradana Taufik
title	Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
title_short	Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
title_full	Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
title_fullStr	Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
title_full_unstemmed	Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
title_sort	utilization of k-nearest neighbor algorithm for classification of white blood cells in aml m4, m5, and m7
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/dff44182cbd54bc3b70102fece130250
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Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7

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