Browse > Article
http://dx.doi.org/10.9717/kmms.2022.25.10.1512

Comparative Analysis of Diagnostic Prediction Algorithm Performance for Blood Cancer Factor Validation and Classification  

Jeong, Jae-Seung (Korea Institute of Science and Technology, Post-Silicon Semiconductor Institute)
Ju, Hyunsu (Korea Institute of Science and Technology, Post-Silicon Semiconductor Institute)
Cho, Chi-Hyun (Department of Laboratory Medicine, Korea University Ansan Hospital)
Publication Information
Journal of Korea Multimedia Society / v.25, no.10, 2022 , pp. 1512-1523 More about this Journal
Abstract
Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.
Keywords
Artificial Intelligence; Digital Health Care; Machine Learning; Diagnostic Prediction Learning; Classifier;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Y. Huang and L. Li, "Naive Bayes Classification Algorithm Based on Small Sample Set," 2011 Institute of Electrical and Electronics Engineers International Conference on Cloud Computing and Intelligence Systems, pp. 34-39, 2011.
2 M.A. Jabbar and S. Shirina, "Heart Disease Prediction System Based on Hidden Naive Bayes Classifier," 2016 International Conference on Circuits, Controls, Communications and Computing, pp. 1-5, 2016.
3 B.A. Thakkar, I.H. Mosin, and M.A. Desai, "Health Care Decision Support System for Swine Flu Prediction Using Naive Bayes Classifier," 2010 International Conference on Advances in Recent Technologies in Communication and Computing Institute of Electrical and Electronics Engineers, pp. 101-105, 2010.
4 S. Chakraborty, S. Kaur, S. Guha, and S.K. Batra, "The Multifaceted Roles of Neutrophil Gelatinase Associated Lipocalin (NGAL) in Inflammation and Cancer," Biochimica et Biophysica Acta - Reviews on Cancer, Vol. 1826, Issue 1, pp. 129-169, 2012.   DOI
5 S.Y. Kristinsson, O. Landgren, J. Samuelsson, M. Bjorkholm, and L.R. Goldin, "Autoimmunity and The Risk of Myeloproliferative Neoplasms," Haematologica, Vol. 95, Issue 7, pp. 1216-1220, 2010.   DOI
6 H.T. Kam, "Random Decision Forests," Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1, pp. 278-282, 1995.
7 H. Takizawa and M.G. Manz, "Impact of Inflammation on Early Hematopoiesis and the Microenvironment," Progress in Hematology, Vol. 106, pp. 27-33, 2017.
8 F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, and S. Ma, et al., "Artificial Intelligence in Healthcare: Past, Present and Future," Stroke and Vascular Neurology, Vol. 2, Issue 4, e000101, 2017.
9 J. Wiens and E.S. Shenoy, "Machine Learning for Healthcare: on The Verge of a Major Shift in Healthcare Epidemiology," Clinical Infectious Diseases, Vol. 66, Issue 1, pp. 149-153, 2018.   DOI
10 T. Davenport and R. Kalakota, "The Potential for Artificial Intelligence in Healthcare," Future Healthcare Journal. Vol. 6, No. 2, pp. 94-98, 2019.   DOI
11 H. Long, S.-H. Lee, S.-G. Kwon, and K.-R. Kwon, "A Deep Learning Method for Brain Tumor Classification Based on Image Gradient," Journal of Korea Multimedia Society, Vol. 25, No. 8, pp. 1233-1241, 2022.   DOI
12 A. Kosvyra, C. Maramis, and I. Chouvarda, "Developing an Integrated Genomic Profile for Cancer Patients with The Use of NGS Data," Emerging Science Journal, Vol. 3, No. 3, pp. 157-167, 2019.   DOI
13 C.H. Cho and J. Cha, "Analysis of Neutrophil Gelatinase-Associated Lipocalin, Vascular Endothelial Growth Factor, and Soluble Receptor for Advanced Glycation End-Products in Bone Marrow Supernatant in Hematologic Malignancies," Clinical Biochemistry, Vol. 80, pp. 19-24, 2020.   DOI
14 D. Dumitru, "Prediction of Recurrent Events in Breast Cancer Using The Naive Bayesian Classification," Annals of the University of Craiova-Mathematics and Computer Science Series, Vol. 36, Issue 2, pp. 92-96, 2009.
15 O. Altay and M Ulas, "Prediction of The Autism Spectrum Disorder Diagnosis with Linear Discriminant Analysis Classifier and K-Nearest Neighbor in Children," 2018 6th International Symposium on Digital Forensic and Security, pp. 1-4, 2018.
16 S.Y. Kristinsson, M. Bjorkholm, M. Hultcrantz, A.R. Derolf, O. Landgren, L. and R. Goldin, "Chronic Immune Stimulation Might Act as a Trigger for The Development of Acute Myeloid Leukemia or Myelodysplastic Syndromes," Journal of Clinical Oncology, Vol. 29, Issue 21, pp. 2897-2903, 2011.   DOI
17 K. Mittal, G. Aggarwal, and P. Mahajan, "Performance Study of K-Nearest Neighbor Classifier and K-Means Clustering for Predicting The Diagnostic Accuracy," International Journal of Information Technology, Vol. 11, Issue 3, pp. 535-540, 2019.   DOI
18 B. Dai, R.C. Chen, S.Z. Zhu, and W.W. Zhang, "Using Random Forest Algorithm for Breast Cancer Diagnosis," 2018 International Symposium on Computer, Consumer and Control, pp. 449-452, 2018.
19 D. Yao, Y. Jing, and Z. Xiaojuan, "A Novel Method for Disease Prediction: Hybrid of Random Forest and Multivariate Adaptive Regression Splines," Journal of Computers, Vol. 8, Issue 1, pp. 170-177, 2013.
20 M. Langarizadeh and F. Moghbeli, "Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review," Acta Informatica Medica, Vol. 24, Issue 5, pp. 364-369, 2016.   DOI
21 K. Vembandasamy, R. Sasipriya, and E. Deepa, "Heart Diseases Detection Using Naive Bayes Algorithm," International Journal of Innovative Science, Engineering & Technology, Vol. 2, Issue 9, pp. 441-444, 2015.
22 S.D. Jadhav and H.P. Channe, "Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques," International Journal of Science and Research, Vol. 5, Issue 1, pp. 1842-1845, 2016.
23 S.S. Soni, C. Dinna, B. Ilona, Y.C. Chang, N. Federico, and L. Paolo, et al., "NGAL: a Biomarker of Acute Kidney Injury and Other Systemic Conditions," International Urology and Nephrology, Vol. 42, Issue 1, pp. 141-150, 2010.   DOI
24 H. Takizawa and M.G. Manz, "Impact of Inflammation on Early Hematopoiesis and The Microenvironment," International Journal of Hematology, Vol. 106, Issue 1, pp. 27-33, 2017.   DOI
25 T. Cover and P. Hart, "Nearest Neighbor Pattern Classification," Institute of Electrical and Electronics Engineers Transactions on Information Theory, Vol. 13, No. 1, pp. 21-27, 1967.   DOI
26 B.W. Silverman and M.C. Jones, "E. Fix and J.L. Hodges (1951): an Important Contribution to Nonparametric Discriminant Analysis and Density Estimation," International Statistical Review / Revue Internationale de Statistique, Vol. 57, No. 3, pp. 233-247, 1989.
27 C.H. Cho, J. Yoon, D.S. Kim, S.J. Kim, H.J. Sung, and S.R. Lee, "Association of Peripheral Blood Neutrophil Gelatinase-Associated Lipocalin Levels with Bone Marrow Neutrophil Gelatinase-Associated Lipocalin Levels and Neutrophil Count in Hematologic Malignancy," Journal of Clinical Laboratory Analysis, Vol. 33, Issue 6, e22920, 2019.   DOI
28 L.M. Zouhal and T. Denoeux, "An EvidenceTheoretic K-NN Rule with Parameter Optimization," Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics, P art C (Applications and Reviews), Vol. 28, Issue 2, pp. 263-271, 1998.
29 L. Ceriani and P. Verme, "The Orignis of the Gini Index: Extracts from Variabilita e Mutabilita (1912) by Corrado Gini," The Journal of Economic Inequality, Vol. 20, No. 3, pp. 421-443, 2012.
30 G.H. John and P. Langley, "Estimating Continuous Distributions in Bayesian Classifiers," arXiv P reprint, arXiv:1302.4964, 2013.
31 D.A. Adjeroh, M. Ryynanen, and K.C. Nwosu, "Multimedia Database Management Issues," Journal of Korea Multimedia Society, Vol. 4, No. 3, pp. 24-33, 1997
32 P.J. Moore, T.J. Lyons, and J. Gallacher, "Random Forest Prediction of Alzheimer's Disease Using Pairwise Selection from Time Series Data," Public Library on Science One, Vol. 14, Issue 2, e0211558, 2019.
33 P. Sinha and P. Sinha, "Comparative Study of Chronic Kidney Disease Prediction Using KNN and SVM," International Journal of Engineering Research and Technology, Vol. 4, Issue 12, pp. 608-612, 2015.   DOI