Abstract
Leukemia disease designates a cancer of the bone marrow and lymphatic system. It occurs when certain blood cells acquire changes i.e. or mutations in their genetic material. Leukemias are classified according to their rate of progression and the type of cells involved. Acute Lymphocytic Leukemia (ALL), Acute Myelogenous Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), and Chronic Myelogenous
Leukemia are the four main kinds of leukemia (CML). The classification of the type of Leukemia is very important to diagnose the disease and determine its progression. In this context, we have used the classifiers of machine learning to identify different forms of leukemia., which facilitates the task of doctors and patients. The main objective of this paper is to determine the most effective methods for the detection of leukemia. According to this context, we have established a comparative study between five classifiers (Support Vector Machine, Random Forest, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes). We have evaluated our system with four metrics: Precision, Accuracy, Recall, and F1-score. The experimental results on Gene Expression Dataset demonstrate that the Support Vector Machine classifier obtains the highest accuracy; however, this accuracy varies depending on the algorithm used to classify the types of leukemia and also on the shape and size of the sample.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ali, N.O.: A Comparative study of cancer detection models using deep learning - leukemia. Deep Learn. 1–48 (2020)
Pham, T., Tran, T., Phung, D., Venkatesh, S.: Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform. 69, 218–229 (2017). https://doi.org/10.1016/j.jbi.2017.04.001
Kumar, Y., Koul, A., Singla, R., Ijaz, M.F.: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J. Amb. Intell. Hum. Comput. 1–28 (2021). https://doi.org/10.1007/s12652-021-03612-z
Larrañaga, P., et al.: Machine learning in bioinformatics. Brief. Bioinform. 7(1), 86–112 (2006). https://doi.org/10.1093/bib/bbk007
Golub, K., et al.: Gene Expression Dataset. https://www.kaggle.com/crawford/gene-expression
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science (80-. )., 286(5439), 531–527 (1999). https://doi.org/10.1126/science.286.5439.531
Ratley, A., Minj, J., Patre, P.: Leukemia disease detection and classification using machine learning approaches: a review. In: 2020 1st Int. Conf. Power, Control Comput. Technol. ICPC2T 2020, pp. 161–165 (2020). https://doi.org/10.1109/ICPC2T48082.2020.9071471
Alrefai, N.: Ensemble machine learning for leukemia cancer diagnosis based on microarray datasets. Int. J. Appl. Eng. Res., 14(21), 4077–4084 (2019). http://www.ripublication.com
Ghaderzadeh, M., Asadi, F., Hosseini, A., Bashash, D., Abolghasemi, H., Roshanpour, A.: Machine learning in detection and classification of leukemia using smear blood images: a systematic review. Sci. Program. 2021 (2021). https://doi.org/10.1155/2021/9933481
Huang, S., Nianguang, C.A.I., Penzuti Pacheco, P., Narandes, S., Wang, Y., Wayne, X.U.: Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteom. 15(1), 41–51 (2018). https://doi.org/10.21873/cgp.20063
Gandhi, R.: Support Vector Machine - Introduction to Machine Learning Algorithms. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Kwong, G.A., Ghosh, S., Gamboa, L., Patriotis, C., Srivastava, S., Bhatia, S.N.: Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat. Rev. Cancer 21(10), 655–668 (2021). https://doi.org/10.1038/s41568-021-00389-3
Italia Joseph Maria, D.R., Devi, T.: Machine Learning Algorithms for Diagnosis of Leukemia
“regression-logistique-quest-ce-que-cest.” https://datascientest.com
“Confusion Matrix in Machine Learning, 23 February 2020.” https://www.geeksforgeeks.org/confusion-matrix-machine-learning/
“(2019, August 5).” https://www.harrisgeospatial.com/docs/CalculatingConfusionMatrices.html. Accessed 05 August 2019
Brownlee, J.: How to calculate precision, recall, and f-measure for from, imbalanced classification. https://machinelearningmastery.com/precision-recall-and-f-measure-for-imbalanced-classification/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Houssaini, Z.S., El beqqali, O., El Riffi, J. (2023). Machine Learning-Based Classification of Leukemia Comparative Study. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-29313-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28845-6
Online ISBN: 978-3-031-29313-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)