Abstract
Cancer is one of the leading and major causes of death nowadays. While developing the approaches for healing the cancer is key, the role of correct classification is always encouraged. In the proposed system, we are going to classify Leukemia into Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML) and also going to predict who is at cancer risk by monitoring gene expressions. Leukemia is a blood or bone marrow type of cancer which can be treated with aggressive chemotherapy, bone marrow, or stem cell transplant therapy in major cases. But many of the Leukemic patients may not need chemotherapy if found in the initial stages. While chemotherapy is known to be effective, but it may not be the best option for every cancer patient. The main motivation is to help society to reduce the cost of treatment for cancer as many people cannot afford the cost of chemotherapy. It is also better to predict the type and intensity of cancer at early stages through gene expression monitoring. To solve this problem, we chose the domain of Bioinformatics, which is an interdisciplinary field mainly involving molecular biology, genetics, statistics, and computer science.
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
References
Chu, F., Xie, W., Wang, L.: Gene selection and cancer classification using a fuzzy neural network, vol. 2, pp. 555–559 (2004)
Nahar, J., Chen, Y.-P.P., Shawkat Ali, A.B.M.: Micro-array classification and rule based cancer identification, pp. 43–46 (2007)
Wang, L., Chu, F., Xie, W.: Accurate cancer classification using expression of very few genes 4(1), 40–53 (2007)
Xu, R., Anagnostopoulos, G.C., Wunsch, D.C.: Multiclass cancer classification using semisupervised ellipsoid ARTMAP and particle swarm optimization with gene expression data 4(1), 65–77 (2007)
Yukinawa, N., Oba, S., Kato. K., Ishii, S.: Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profile 6(2), 333–343 (2009)
Luo, W., Wang, L., Sun, J.: Feature selection for cancer classification based on support vector machine 4, 422–426 (2009)
Bi̇len, M., Işik, A.H., Yi̇ği̇t, T.: A hybrid artificial neural network genetic algorithm approach for classification of micro array data, pp. 339–342 (2015)
Ganesan, T., Rajarajeswari, P.: Genetic algorithm based optimization to improve the cluster lifetime by optimal sensor placement in WSN’s. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(8) (2019). ISSN: 2278-3075
Tasci, A., Ince, T., Guzelis, C.: A comparison of feature selection algorithms for cancer classification through gene expression data. In: 2017 10th International onference on Electrical and Electronics Engineering (ELECO), pp. 1352–1354 (2017)
Ganesan, T., Rajarajeswari, P.: Genetic algorithm approach improved by 2D lifting scheme for sensor node placement in optimal position. In: Second International Conference on Intelligent Sustainable Systems IEEE xplore, ISBN: 978-1-5386-7798-8978 (2019)
Harvey, B.S., Ji, S.-Y.: Cloud-scale genomic signals processing for robust large scale cancer genomic micro-array data analysis 21(1), 238–245 (2017)
Kavitha, K.R., Nair Harishankar, U., Akhil, M.C.: PSO based feature selection of gene for cancer classification using SVM-RFE, pp. 1012–1016 (2018)
Guillen, P., Ebalunode, J.: Cancer classification based on microarray gene expression data using deep learning, pp. 1403–1405 (2016)
Zhu, Q.-X., Fan, Y., He, Y.-L., Xu, Y.: Effective cancer classification based on gene expression data using multidimensional mutual information ELM, pp. 954–958 (2018)
Ma, X., Tang, W., Wang, P., Guo, X., Gao, L.: Extracting stage specific and dynamic modules through analysing multiple networks associated with cancer progression 15(2), 647–658 (2018)
Golub, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537
Supriyamenon, M., Rajarajeswari, P.: A review on association rule mining techniques with respect to their privacy preserving capabilities (KLEF). Int. J. Appl. Eng. Res. 12(24), 15484–15488 (2017). ISSN 0973-4562
Amulya, P., Sai Meghana, S., Manisha, R.P.: A deep learning approach for brain tumor segmentation using convolution neural network. Int. J. Sci. Technol. Res. (2019)
Rajarajeswari, P., Supriya menon, M.: A contemporary way for enhancedmodeling of context aware privacy system in PPDM. J. Adv. Res. Dyn. Control Syst. 10(01) (2018)
Krishna, N., Rajarajeswari, P., et al.: Recognition of fake currency note using convolutional neural networks. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(5) (2019). ISSN: 2278-3075
Jafarpisheh, N., Teshnehlab, M.: Cancer classification based on deep neural networks and emotional learning approach 12(6), 258–263 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Rajarajeswari, P., Navya Krishna, G., Sai Pooja, G., Yamini Radha, V., Naga Sri Ram, B. (2021). Classification and Prediction of Leukemia Using Gene Expression Profile. In: Srinivasa, K.G., G. M., S., Sekhar, S.R.M. (eds) Artificial Intelligence for Information Management: A Healthcare Perspective. Studies in Big Data, vol 88. Springer, Singapore. https://doi.org/10.1007/978-981-16-0415-7_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-0415-7_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0414-0
Online ISBN: 978-981-16-0415-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)