386 425
Full Length Article
Fusion: Practice and Applications
Volume 12 , Issue 2, PP: 145-158 , 2023 | Cite this article as | XML | Html |PDF

Title

A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor

  K. Venkatesh 1 * ,   S. Pasupathy 2 ,   S. P. Raja 3

1  Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
    (venkiur01@gmail.com)

2  Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
    (pathyannamalai@gmail.com)

3  School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
    (avemariaraja@gmail.com)

Full Text Download


Doi   :   https://doi.org/10.54216/FPA.120212

Received: January 27, 2023 Revised: April 24, 2023 Accepted: June 18, 2023

Abstract :

Analysis of microarray data is extremely complex and considered as a hot topic in recent research. Acute Myeloid Leukemia (AML) prediction based on machine learning shows huge impact on prediction which automatically diagnoses the disease severity and any malfunctions. It is important to design the relevant classifier that processes the large data volume with large data size. Deep learning is an updated machine learning approach for mitigating these issues. It is easy to handle the huge volume of data because of the large number of hidden layers. The proposed classification methodology is used for understanding the training of the proposed Dense Polynomial Dimensionality based Predictor Model (). The hidden neuron numbers are large in a sufficient way where the proposed  is elaborated to predict AML. AML and ALL samples are classified using five layers in the deep network model. The data is partitioned as 20% data and 80% data testing and training in the network. Compared with other classifiers, the satisfying outcome from the proposed  is higher and fulfilling. The validation is done in three datasets: Kaggle, Gene expression and Bio GPS and it gives 96% accuracy, 94% precision, 96% recall, 96% F1-score, and 98% AUROC while executing with Kaggle; then, 95.50% accuracy, 94% precision, 95% recall, 96% F1-score, and 96% AUROC is achieved while executing with Gene expression and finally 98% accuracy, 94.5% precision, 98.5% recall, 96% F1-score, and 94% AUROC is achieved while executing with Bio GPS. Based on this analysis, it is proven that the model works well with the proposed  and establishes a better trade-off.

Keywords :

Acute leukaemia , prediction; deep learning; validation; dense network model

References :

[1] R. J. Leary, M. Sausen, I. Kinde, N. Papadopoulos, J. D. Carpten, D. Craig, D. O’Shaughnessy, K. W. Kinzler, G. Parmigiani, B. Vogelstein, et al., “Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing,” Sci. Transl. Med., vol. 4, no. 162, pp. 2012.

[2] H. B. Hsieh, D. Marrinucci, K. Bethel, D. N. Curry, M. Humphrey, R. T. Krivacic, J. Kroener, L. Kroener, R. Ladanyi, N. Lazarus, N., et al., “High-speed detection of circulating tumor cells,” Bio-sensors Bioelectron, vol. 21, no. 10, pp. 1893–1899, 2006.

[3] M. Fatma, J. Sharma, “Identification and classification of acute leukemia using neural network,” In 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), pp. 142–145, 2014.

[4] L. Pan, G. Liu, F. Lin, S. Zhong, H. Xia, X. Sun, H. Liang, “Machine learning applications for predicting relapse in childhood acute lymphoblastic leukemia,” Sci. Reports, vol. 7, no. 1, pp. 1–9, 2017.

[5] V. Kumar, S. Ailawadhi, L. Bojanini, A. Mehta, S. Biswas, T. Sher, V. Roy, P. Vishnu, J. Marin-Acevedo, V. R. Alegria, “Trends in the risk of second primary malignancies among survivors of chronic lymphocytic leukemia,” Blood Cancer J. vol. 9, no. 10, pp. 1–10, 2019.

[6] Y. N. Chung, H. N. Kim, S. R. Lee, H. J. Sung, M. H. Nam. “Usefulness of chromosomal microarray in hematologic malignancies: a case of aggressive NK-cell leukemia with 1Q abnormality,” Lab Med., vol. 9, no. 3, pp.189–93, 2019.

[7] Y. Ochi, K. Yoshida, Y. J. Huang, M. C. Kuo, K. Sasaki, N. Hosoya, N. Hiramoto, R. Bera, Y. Nannya, Y. Shiozawa, “Prognostic relevance of genetic abnormalities in the blastic transformation of chronic myeloid leukemia,” Blood, vol. 136, pp. 3–4, 2020.

[8] M. Houshmand, G. Simonetti, P. Circosta, V. Gaidano, A. Cignetti, G. Martinelli, G. Saglio, R. P. Gale, “Chronic myeloid leukemia stem cells. Leukemia,” vol. 33, no. 7, pp. 1543–56, 2019.

[9] S. Kollmann, E. Grundschober, B. Maurer, W. Warsch, R. Grausenburger, L. Edlinger, J. Huuhtanen, S. Lagger, L. Hennighausen, P. Valent, “Twins with different personalities: STAT5B—but not STAT5A—has a key role in BCR/ ABL-induced leukemia,” leukemia, vol. 33, no. 7, pp. 1583–97, 2019.

[10] O. Taiwo, F. Kasali, I. Akinyemi, S. Kuyoro, O. Awodele, D. Ogbaro, T. Olaniyan, “Stratification of chronic myeloid leukemia cancer dataset into risk groups using four machine learning algorithms with minimal loss function,” Afr J Manag Inf Syst, vol. 1, pp. 1–18, 2019.

[11] L. Yu, X. Huang, R. P. Gale, H. Wang, Q. Jiang “Variables associated with patient-reported symptoms in persons with chronic phase chronic myeloid leukemia receiving tyrosine kinase inhibitor therapy,” medicine, vol. 98, no. 48, pp. e18079, 2021.

[12] C. M. Lynch, B. Abdollahi, J. D. Fuqua, A. R. de Carlo, J. A. Bartholomai, R. N. Balgemann, V. H. van Berkel, H. B. Frieboes, “Prediction of lung cancer patient survival via supervised machine learning classification techniques,” Int J Med Inform, vol. 108, pp. 1–8, 2017.

[13] A. Mosquera Orgueira, A. Peleteiro Raíndo, M. Cid López, J. A. Díaz Arias, M. S. González Pérez, B. Antelo Rodríguez, N. Alonso Vence, L. Bao Pérez, R. Ferreiro Ferro, M. Albors Ferreiro, “Personalized survival prediction of patients with acute myeloblastic leukemia using gene expression profiling,” Front Oncol, vol. 11, pp. 1018, 2021.

[14] K. Sasaki, E. J. Jabbour, F. Ravandi, M. Konopleva, G. Borthakur, W. G. Wierda, N. Daver, K. Takahashi, K. Naqvi, C. DiNardo, “The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: a model to improve patient outcomes,” Am J Hematol, vol. 96, no. 2, pp. 241–50, 2021.

[15] O. Koteluk, A. Wartecki, S. Mazurek, I. Kołodziejczak, A. Mackiewicz, “How do machines learn? Artificial intelligence as a new era in medicine,” J Personal Med, vol. 11, no.1, pp. 32, 2021.

[16] A. G. Singal, A. Mukherjee, B. J. Elmunzer, P. D. Higgins, A. S. Lok, J. Zhu, J. A. Marrero, A. K. Waljee, “Machine learning algorithms outperform conventional regression models in predicting the development of hepatocellular carcinoma,” Am J Gastroenterol, vol. 108, no. 11, pp. 1723, 2013.

[17] Y. Feng, X. Wang, J. Zhang, “A heterogeneous ensemble learning method for neuroblastoma survival prediction,” IEEE J Biomed Health Inform, vol. 26, pp. 1472–83, 2021.

[18] A. Jamshidi, J. P. Pelletier, J. Martel-Pelletier, “Machine-learning-based patient-specific prediction models for knee osteoarthritis,” Nat Rev Rheumatol, vol. 15, no. 1, pp. 49–60, 2019.

[19] R. Jayashanka, C. Wijesinghe, A. Weerasinghe, D. Pieris, “Machine learning approach to predict the survival time of childhood acute lymphoblastic leukemia patients,” In Proc. 18th international conference on Advances in ICT for emerging regions (ICTer), pp. 426–432, 2018.

[20] J. N. Eckardt, C. Rollig, M. Kramer, S. Stasik, J. A. Georgi, P. Heisig, F. P. Kroschinsky, J. Scheteli, U. Platzbecker, C. Müller-Tidow, “Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning,” Blood, vol. 138, pp. 108, 2021.

[21] K. Karami, M. Akbari, M. T. Moradi, B. Soleymani, H. Fallahi, “Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques,” PLoS ONE, vol. 16, no. 7, pp. e0254976, 2021.

[22] K. N. Neeraj, V. Maurya, “A review on machine learning (feature selection, classification and clustering) approaches of big data mining in a different area of research,” J Crit Rev, vol. 7, no. 19, pp. 2610–26, 2020.

[23] A. M. Alqudah, “Ovarian cancer classification using serum proteomic profiling and wavelet features a comparison of machine learning and features selection algorithms,” J Clin Eng, vol. 44, no. 4, pp. 165–73, 2019.

[24] X. Gu, J. Guo, L. Xiao, T. Ming, C. Li, “A feature selection algorithm based on equal interval division and minimal-redundancy–maximal-relevance,” Neural Process Lett, vol. 51, no. 2, pp. 1237–63, 2020.

[25] D. Chen, G. Goyal, R. Go, S. Parikh, C. Ngufor, “Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods,” In Proc. IEEE international conference on healthcare informatics (ICHI), pp. 407–408, 2018.

[26] A. Kashefzadeh, L. Ohadi, M. Golmohammadi, F. Araghi, S. Dadkhahfar, A. Kiani, A. Abedini, A. Fadaii, A. Ghojoghi, M. Nouraie, et al., “Clinical features and short-term outcomes covid-19 in Tehran, Iran: an analysis of mortality and hospital stay,” Acta Biomed, vol. 91, no. 4, pp. 1–10, 2020.

[27] X. Hu, B. Wang, Q. Chen, A. Huang, W. Fu, L. Liu, Y. Zhang, G. Tang, H. Cheng, X. Ni, “A clinical prediction model identifies a subgroup with inferior survival within intermediate-risk acute myeloid leukemia,” J Cancer, vol. 12, no. 16, pp. 4912–23, 2021.

[28] S. Rinesh, K. Maheshwari, B. Arthi, P. Sherubha, A. Vijay, S. Sridhar, T. Rajendran, Y. A. Waji, “Investigations on brain tumor classification using hybrid machine learning algorithms”, Hindawi, Journal of Healthcare Engineering, 2022.

[29] S. Dasariraju, M. Huo, S. McCalla, “Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random Forest algorithm,” Bioengineering (Basel), vol. 7, pp. 120, 2020.

[30] A. Krizhevsky, I. Sutskever, G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun ACM, vol. 60, pp. 84–90, 2017.

[31] Nada M. Sallam, “An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images” Alexandria Engineering Journal, Vol. 68, pp. 39-66, 2023.

[32] Fallah H Najjar1, Kifah T Khudhair2, Zaid Nidhal Khudhair3,4, Haneen H Alwan2 and Ameer Al-khaykan, “Acute lymphoblastic leukemia image segmentation based on modified HSV model”, J. Phys.: Conf. Ser. 2432 012020.

[33] Petru Manescu, Priya Narayanan, Christopher Bendkowski, Muna Elmi, Remy Claveau, Vijay Pawar, Biobele J Brown, Mike Shaw, Anupama Rao, Delmiro Fernandez-Reyes, “Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning”, Sci Rep, Vol.3. 13(1), pp.2562-70, 2019.

[34] S. Hemamalini ,V. D. Ambeth Kumar ,R. Venkatesan,S. Malathi. (2023). Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification. Journal of Fusion: Practice and Applications, 11 ( 2 ), 90-110.

[35] C. S. Manigandaa,V. D. Ambeth Kumar,G. Ragunath,R. Venkatesan,N. Senthil Kumar. (2023). De-Noising and Segmentation of Medical Images using Neutrophilic Sets. Journal of Fusion: Practice and Applications, 11 ( 2 ), 111-123

[36] Sathya Preiya, V., and V. D. Ambeth Kumar. 2023. "Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes" Diagnostics 13, no. 12: 1983. https://doi.org/10.3390/diagnostics13121983

[37] Balakrishnan, Chitra, and V. D. Ambeth Kumar. 2023. "IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks" Diagnostics 13, no. 4: 775. https://doi.org/10.3390/diagnostics13040775.

[38] V. D. Ambeth Kumar,S. Malathi,Abhishek Kumar,Prakash M and Kalyana C. Veluvolu, “Active Volume Control in Smart Phones Based on User Activity and Ambient Noise” ,Sensors 2020, 20(15), 4117; https://doi.org/10.3390/s20154117

[39] V. Sathya Preiya,V. D. Ambeth Kumar,R. Vijay,Vijay K.,N. Kirubakaran. "Blockchain-Based E-Voting System with Face Recognition." Fusion: Practice and Applications, Vol. 12, No. 1, 2023 ,PP. 53-63

Cite this Article as :
Style #
MLA K. Venkatesh, S. Pasupathy, S. P. Raja. "A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor." Fusion: Practice and Applications, Vol. 12, No. 2, 2023 ,PP. 145-158 (Doi   :  https://doi.org/10.54216/FPA.120212)
APA K. Venkatesh, S. Pasupathy, S. P. Raja. (2023). A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor. Journal of Fusion: Practice and Applications, 12 ( 2 ), 145-158 (Doi   :  https://doi.org/10.54216/FPA.120212)
Chicago K. Venkatesh, S. Pasupathy, S. P. Raja. "A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor." Journal of Fusion: Practice and Applications, 12 no. 2 (2023): 145-158 (Doi   :  https://doi.org/10.54216/FPA.120212)
Harvard K. Venkatesh, S. Pasupathy, S. P. Raja. (2023). A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor. Journal of Fusion: Practice and Applications, 12 ( 2 ), 145-158 (Doi   :  https://doi.org/10.54216/FPA.120212)
Vancouver K. Venkatesh, S. Pasupathy, S. P. Raja. A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor. Journal of Fusion: Practice and Applications, (2023); 12 ( 2 ): 145-158 (Doi   :  https://doi.org/10.54216/FPA.120212)
IEEE K. Venkatesh, S. Pasupathy, S. P. Raja, A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor, Journal of Fusion: Practice and Applications, Vol. 12 , No. 2 , (2023) : 145-158 (Doi   :  https://doi.org/10.54216/FPA.120212)
↑