Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection
Revella E. A. Armya, Adnan Mohsin Abdulazeez, Amira Bibo Sallow, Diyar Qader Zeebaree
Asian Journal of Research in Computer Science · pp. 52–65 · Published 15 Jun 2021
10.9734/ajrcos/2021/v9i330225Abstract
Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types.
References (28)
- 1 Classification Based on Decision Tree Algorithm for Machine Learning [DOI]
- 2 A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems [DOI]
- 3 Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks [DOI]
- 4 Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States [DOI]
- 5 IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning [DOI]
- 6 Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images [DOI]
- 7 Leukemia Blood Cell Image Classification Using Convolutional Neural Network [DOI]
- 8 Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer [DOI]
- 9 Gene Selection and Classification of Microarray Data Using Convolutional Neural Network [DOI]
- 10 An enhanced J48 classification algorithm for the anomaly intrusion detection systems [DOI]
- 11 Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm [DOI]
- 12 Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques [DOI]
- 13 Acute leukemia classification by using SVM and K-Means clustering [DOI]
- 14 Medical Images Segmentation Based on Unsupervised Algorithms: A Review [DOI]
- 15 Identification of Full and Partial Class Relevant Genes [DOI]
- 16 Convergent learning–based model for leukemia classification from gene expression [DOI]
- 17 Detection and Classification of Acute Lymphocytic Leukemia [DOI]
- 18 An Analytical Appraisal for Supervised Classifiers’ Performance on Facial Expression Recognition Based on Relief-F Feature Selection [DOI]
- 19 GFNB: Gini index–based Fuzzy Naive Bayes and blast cell segmentation for leukemia detection using multi-cell blood smear images [DOI]
- 20 Machine Learning based System for Automatic Detection of Leukemia Cancer Cell [DOI]
- 21 Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia [DOI]
- 22 Data Mining Implementation to Predict Sales Using Time Series Method [DOI]
- 23 Data Mining Attribute Selection Approach for Drought Modelling : A Case Study for Greater Horn of Africa [DOI]
- 24 Gene clustering with partition around mediods algorithm based on weighted and normalized mahalanobis distance [DOI]
- 25 A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models [DOI]
- 26 Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features [DOI]
- 27 custom: A Review of most Recent Lung Cancer Detection Techniques using Machine Learning
- 28 Data mining using filtering approaches and ensemble methods [DOI]
Cited by 13
Jisha John, Felix M. Philip · 2025 International Conference on NexGen Networks and Cybernetics (IC2NC) · 2025
Waqar Ahmad, Abdul Raheem Shahzad, Muhammad Awais Amin · PLoS ONE · 2025
N. V, P. Bai S · International Congress on Information and Communication Technology · 2025
Khalid Mahboob, Umm-e Laila, Mahvash Arsalan Lodhi · 2024 Global Conference on Wireless and Optical Technologies (GCWOT) · 2024
Autcha Araveeporn, Puntipa Wanitjirattikal · International Conference on Mathematics and Statistics · 2024
Hakar Hasan Rasheed, A. Abdulazeez · Indonesian Journal of Computer Science · 2024
Amogh Ramagiri, V. Jahnavi, Sathwik Gottipati · 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · 2023
Humberto O. Bragança, Richard F. Pinto, Bruno L. Dalmazo · Lecture Notes in Computer Science · 2025
Showing 8 of 13 known citations — external sources report more than can currently be individually listed.
Related research
- Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
- Prediction of Radiotherapy Dose Distribution for Glioblastoma Using Convolutional Neural Network Model — shares topic coverage
- A Systematic Literature Review of Machine Learning Methods in Healthcare — shares topic coverage
- Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
- Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage
Article metrics
Real usage data collected on this platform.
26
Page views
0
PDF downloads
3
Outbound clicks
13
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.