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Prediction of Acute Myeloid Leukemia Subtypes Based on Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Approaches

  • Etee Kawna RoyEmail author
  • Subrata Kumar Aditya
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 33)

Abstract

The proposed technique involves designing and implementing an acute myeloid leukemia sub-type prediction system based on artificial neural network and adaptive neuro-fuzzy inference system approaches. The dataset of 600 possible cases (patients) of acute myeloid leukemia is used. After training the system with 540 input–output dataset of patients having AML-M0, AML-M1, AML-M2, AML-M3, and AML-M4 types of leukemia, it is tasted with 60 data for validation. The method is implemented to predict these five types of acute myeloid leukemia based on the characteristics of four complete blood count (CBC) parameters, namely leukocytes, hemoglobin, platelets, and blasts of the patients. The neural network performed well than the adaptive neuro-fuzzy inference system when test data was considered, where the average mean squared error (MSE) for each system was 0.0433 and 0.2089, respectively. The adaptive neuro-fuzzy inference system showed better performance than artificial neural network when training data was considered, where the mean squared error (MSE) for each system was 0.0017 and 0.0044, respectively.

Keywords

Acute myeloid leukemia Inference system Perceptron Epoch Hematology Membership function 

References

  1. 1.
    Pouls RK, Shamoon RP, Muhammed NS (2012) Clinical and hematological parameters in adult AML patients: a four year experience at Nanakaly Hospital for blood diseases. Zanco J Med Sci 16:199–203CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Negnevitsky M (2011) Artificial intelligence: a guide to intelligent systems. Pearson Education Ltd., EnglandGoogle Scholar
  4. 4.
    Karabatak M, Ince CM (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36:3465–3469CrossRefGoogle Scholar
  5. 5.
    Übeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33:1054–1062CrossRefGoogle Scholar
  6. 6.
    Tan TZ, Quek C, Ng GS et al (2007) A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Syst Appl 33:652–666CrossRefGoogle Scholar
  7. 7.
    Lisboa PJ, Etchells TA, Jarman IH et al (2008) Time-to-event analysis with artificial neural networks: an integrated analytical and rule-based study for breast cancer. Neural Netw 21:414–426Google Scholar
  8. 8.
    Stephan C, Cammann H, Meyer HA et al (2008) An artificial neural network for five different assay systems of prostate specific antigen in prostate cancer diagnostics. BJU Int 102:799–805CrossRefGoogle Scholar
  9. 9.
    Stephan C, Xu C, Finne P et al (2007) Comparison of two different artificial neural networks for prostate biopsy indication in two different patient populations. Urology 70:596–601CrossRefGoogle Scholar
  10. 10.
    Kattan WM (2008) Editorial comment on: development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy. Eur Urol 54:611 (epub January 15)Google Scholar
  11. 11.
    Kawakami S, Numao N, Okubo Y et al (2008) Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy. Eur Urol 54:601–611 (epub January 15)Google Scholar
  12. 12.
    Chun FKH, Graefen M, Briganti A et al (2007) Initial biopsy outcome prediction head-to-head comparison of a logistic regression-based nomogram versus artificial neural network. Eur Urol 51:1236–1243CrossRefGoogle Scholar
  13. 13.
    Kurt I, Ture M, Kurum AT (2008) Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl 34:366–374CrossRefGoogle Scholar
  14. 14.
    Mucke L Strategies to prevent neural network dysfunction in Alzheimer. Alzheimer’s Dementia 4(Supp 1):T102–T103Google Scholar
  15. 15.
    Luca, MD, Grossi E, Borroni B et al (2005) Artificial neural networks allow the use of simultaneous measurements of alzheimer disease markers for early detection of the disease. J Transl Med 3Google Scholar
  16. 16.
    Uğuz H, Öztürk A, Saraçoğlu R et al (2008) A biomedical system based on fuzzy discrete hidden markov model for the diagnosis of the brain diseases. Expert Syst Appl 35:1104–1114CrossRefGoogle Scholar
  17. 17.
    Mataria M, Janech GM, Almeida J et al (2008) Prediction of progression of diabetic nephropathy in a small set of patients by artificial neural networks and proteomic analysis. Am J Kidney Dis 51:B67–B67CrossRefGoogle Scholar
  18. 18.
    Polata K, Karab S, Güvenc A et al (2008) Utilization of discretization method on the diagnosis of optic nerve disease. Comput Methods Programs Biomed 91:255–264Google Scholar
  19. 19.
    Tan TZ, Quek C, Ng SG et al (2008) Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif Intell Med 43:207–222CrossRefGoogle Scholar
  20. 20.
    Săftoiu A et al (2008) Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer. Gastrointest Endosc 68:1086–1094CrossRefGoogle Scholar
  21. 21.
    Ari S, Saha G (2009) In search of an optimization technique for artificial neural network to classify abnormal heart sounds. Appl Soft Comput 9:330–340CrossRefGoogle Scholar
  22. 22.
    Qiua X, Taob N, Tana Y et al (2007) Constructing of the risk classification model of cervical cancer by artificial neural network. Expert Syst Appl 32:1094–1099CrossRefGoogle Scholar
  23. 23.
    Chang CL, Chena CH (2009) Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Syst Appl 36:4035–4041CrossRefGoogle Scholar
  24. 24.
    Noronha EP et al (2011) Immunophenotyping characterization of acute leukemia at a public oncology reference center in Maranhão, Northeastern, Brazil. Sao Paulo Med J 129Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringUniversity of DhakaDhakaBangladesh

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