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

Abstract

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.

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

“…Raman spectroscopy of serum samples, coupled to the kNN classification model, has been used as a diagnostic technique for endometriosis [36]. The kNN algorithm has also been used for the classification of white blood cells in different types of acute myeloid leukaemia according to cells' morphological characteristics [37].…”

Z-Score Normalized Features with Maximum Distance Measure Based k-NN Automated Blood Cancer Diagnosis System

“…[8] and unweighted distance metrics in Eqn. [1], [6] and [7]. The six features such as area, nucleus ratio, circularity, perimeter, mean, and standard deviation are used for classification (17).…”

“…[7] gives the Euclidean distance shown in Eqn. [1] and p = ∞, gives the Chebyshev distance shown in Eqn. [6].…”

“…Depending on the development, it can be classified into two types: acute and chronic. Acute Leukemia multiple the blast cells very fast, and in chronic Leukemia, it multiple very slowly (1). According to French-American-British, Acute Leukemia is further classified into Acute Myeloid Leukemia (AML) common in children and Acute Lymphoblastic Leukemia (ALL) among children and adults, and its growth is rapid.…”

“…The k-NN learning algorithm classifies the normalized data samples into cancerous and non-cancerous shown in Fig (1). The similarity between the features Measured by distance measure and GridSearch cross-validation is used to obtain the k value with the best accuracy (15).…”