Amazing technological breakthrough possible @S-Logix

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • +91- 81240 01111

Social List

A subtype classification of hematopoietic cancer using machine learning approach - 2021

A Subtype Classification Of Hematopoietic Cancer Using Machine Learning Approach

Classification of hematopoietic cancer using machine learning approach | S - Logix

Research Area:  Machine Learning


Hematopoietic cancer is the malignant transformation in immune system cells. This cancer usually occurs in areas such as bone marrow and lymph nodes, the hematopoietic organ, and is a frightening disease that collapses the immune system with its own mobile characteristics. Hematopoietic cancer is characterized by the cells that are expressed, which are usually difficult to detect in the hematopoiesis process. For this reason, we focused on the five subtypes of hematopoietic cancer and conducted a study on classifying by applying machine learning algorithms both contextual approach and non-contextual approach. First, we applied PCA approach for extracting suited feature for building classification model for subtype classification. And then, we used four machine learning classification algorithms (support vector machine, k-nearest neighbor, random forest, neural network) and synthetic minority oversampling technique for generating a model. As a result, most classifiers performed better when the oversampling technique was applied, and the best result was that oversampling applied random forest produced 95.24% classification performance.

hematopoietic cancer
frightening disease
machine learning algorithm
contextual approach
support vector machine
k-nearest neighbor
random forest
neural network

Author(s) Name:  Kwang Ho ParkVan Huy PhamKhishigsuren DavagdorjLkhagvadorj MunkhdalaiKeun Ho Ryu

Journal name:  Asian Conference on Intelligent Information and Database Systems

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/978-981-16-1685-3_10

Volume Information:  pp 113-121