DOI QR코드

DOI QR Code

혈액암 인자 유효성 검증과 분류를 위한 진단 예측 알고리즘 성능 비교 분석

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)
  • 투고 : 2022.07.25
  • 심사 : 2022.10.04
  • 발행 : 2022.10.31

초록

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.

키워드

과제정보

This research was supported by the Korean National Police Agency (KNPA)-(PR08-04-000-21).

참고문헌

  1. 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.
  2. 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. https://doi.org/10.1093/cid/cix731
  3. T. Davenport and R. Kalakota, "The Potential for Artificial Intelligence in Healthcare," Future Healthcare Journal. Vol. 6, No. 2, pp. 94-98, 2019. https://doi.org/10.7861/futurehosp.6-2-94
  4. 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. https://doi.org/10.9717/KMMS.2022.25.8.1233
  5. 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. https://doi.org/10.28991/esj-2019-01178
  6. 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. https://doi.org/10.1016/j.clinbiochem.2020.04.002
  7. 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.
  8. 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. https://doi.org/10.15623/ijret.2015.0404105
  9. 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. https://doi.org/10.1007/s41870-018-0233-x
  10. 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.
  11. 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.
  12. 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.
  13. 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. https://doi.org/10.5455/aim.2016.24.364-369
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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. https://doi.org/10.1007/s11255-009-9608-z
  20. 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. https://doi.org/10.1016/j.bbcan.2012.03.008
  21. 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. https://doi.org/10.1007/s12185-017-2266-5
  22. 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. https://doi.org/10.1200/JCO.2011.34.8540
  23. 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. https://doi.org/10.3324/haematol.2009.020412
  24. 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. https://doi.org/10.1002/jcla.22920
  25. 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.
  26. 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.
  27. 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.
  28. 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. https://doi.org/10.1109/TIT.1967.1053964
  29. H.T. Kam, "Random Decision Forests," Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1, pp. 278-282, 1995.
  30. 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.
  31. G.H. John and P. Langley, "Estimating Continuous Distributions in Bayesian Classifiers," arXiv P reprint, arXiv:1302.4964, 2013.
  32. H. Takizawa and M.G. Manz, "Impact of Inflammation on Early Hematopoiesis and the Microenvironment," Progress in Hematology, Vol. 106, pp. 27-33, 2017.
  33. 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