ISSN : 2319-7323

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE ENGINEERING

ABSTRACT

Title	:	Machine Learning Techniques for the Classification of Blood Cells and Prediction of Diseases
Authors	:	Nisha Varghese
Keywords	:	Hemetological parameters; RBC; WBC and CNN;SVM
Issue Date	:	Jan-Feb 2020
Abstract	:	Machine learning is defined as the acquisition of knowledge that is which takes data and learn from the data. The present study contains the classification of the four major components of blood such as RBCs, WBCs, Platelets, and plasma using the machine learning techniques. A complete blood count (CBC) is used to measure the features of the hematological parameters. The blood components and haematological parameters can be changed by some diseases, smoking, medicines and physical problems. Machine learning can detect the infected or distorted cells and then predict the problems and diseases due to the changes in cells and haematological parameters. Machine Learning is a pool of more than 100 classification algorithms, 75 preprocessing methods, 25 feature selection, and about 20 clustering and association rule methods. The study exhibited that the powerful techniques in the machine learning effectively classified, detected and predicted subtypes of blood cells, changes in count, shape, texture, and color of the blood cells. Convolutional Neural Networks and Support Vector Machine provide highly accurate results.
Page(s)	:	66-75
ISSN	:	2319-7323
Source	:	Vol. 9, No. 1