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Decision Support Technique for Prediction of Acute Lymphoblastic Leukemia Subtypes Based on Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

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Inventive Systems and Control

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

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Abstract

Leukemia is usually characterized as a potentially fatal disease with different types and subtypes. It pretends as one of the major reason for death in developed and developing nations like Bangladesh, almost a hundred thousand people around the world are losing the fight against leukemia in every decade. An early prediction and diagnosis of a leukemia type have become inexorable in treatment, as it can reduce deadly risk to the valuable life of affected patients. This paper's objective is to design and development a smart decision support prediction system of Acute Lymphoblastic Leukemia Subtypes using Artificial Neural Network (i.e. ANN) and Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS). For this prediction system, 500 datasets (main database) of different subtypes of Acute Lymphoblastic Leukemia patients are used. Training and testing are the two main steps of ANN and ANFIS, where 445 and 55 datasets are elected for training and testing respectively. According to the experimental result, it is apparent that the Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS) approach performs better than the Artificial Neural Network (i.e. ANN) in both training and testing. Analysis reveals that a simple and well ergonomic back-propagation learning technique of Artificial Neural Network (i.e. ANN) and a hybrid optimization learning technique of the Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS) can easily predict blood disorder efficiently and effectively in terms of hematological parameters.

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References

  1. Who.int, Cancer (2021). [online] Available at: https://www.who.int/news-room/fact-sheets/detail/cancer. [Accessed 25 April 2021]

  2. M. Payandeh, M. Aeinfar, V. Aeinfar, M. Hayati, A new method for diagnosis and predicting blood disorder and cancer using artificial intelligence (artificial neural networks). Int. J. Hematology-Oncology Stem Cell Res. 3(4), 25–33 (2009)

    Google Scholar 

  3. W. Xu, W. Liu, L. LI, L. MA, S. Xia, J. Zhang, G. Shao, A new computer-aided detection system of CR mammograms. J. Comput. Inform. Syst. 6(9), 2885–2900 (2010)

    Google Scholar 

  4. S. Moein, S.A. Monadjemi, P. Moallem, A novel fuzzy-neural based medical diagnosis system. Int. J. Biol. Med. Sci. 4(3),146–150 (2009)

    Google Scholar 

  5. S. Manoharan, Study on hermitian graph wavelets in feature detection. J. Soft Comput. Paradigm (JSCP) 1(01), 24–32 (2019)

    Google Scholar 

  6. T. Sen, S. Das, An approach to pancreatic cancer detection using artificial neu-ral network, in Proc. of the Second Intl. Conf. on Advances in Computer, Electronics and Electrical Engineering-CEEE (pp. 56–60) (2013)

    Google Scholar 

  7. E. Al-Daoud, Cancer diagnosis using modified fuzzy network. Uni. J. Comput. Sci. Eng. Technol. 1(2), 73–78 (2010)

    Google Scholar 

  8. H. Hamdan, J.M. Garibaldi, Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival, in International Conference on Fuzzy Systems (pp. 1–8) (IEEE, 2010)

    Google Scholar 

  9. M. Negnevitsky, Artificial intelligence: a guide to intelligent systems. Pearson Edu. (2005)

    Google Scholar 

  10. T. BuiTenkamp, Clinical relevance of genetic alterations in acute lymphoblastic leukemia in children with down syndrome (2014)

    Google Scholar 

  11. Cancer.org. (2021). About acute lymphocytic leukemia (ALL). [online] Available at: https://www.cancer.org/cancer/acute-lymphocytic-leukemia/about.html [Accessed 25 April 2021].

  12. T. Terwilliger, M.J.B.C.J. Abdul-Hay, Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 7(6), pp.e577–e577 (2017)

    Google Scholar 

  13. N. Tariq, Breast cancer detection using artificial neural networks. J. Mole. Biomark. Diag. 9(1), 1–6 (2017)

    Google Scholar 

  14. A. Bhardwaj, A. Tiwari, Breast cancer diagnosis using genetically optimized neural network model. Expert Syst. Appl. 42(10), 4611–4620 (2015)

    Google Scholar 

  15. E.D. Übeyli, Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer. J. Med. Syst. 33(5), 353–358 (2009)

    Google Scholar 

  16. M. Karabatak, M.C. Ince, An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36(2), 3465–3469 (2009)

    Google Scholar 

  17. H. Hamdan, J.M.Garibaldi, Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival. in International Conference on Fuzzy Systems (pp. 1–8) (IEEE, 2010)

    Google Scholar 

  18. L. Benecchi, Neuro-fuzzy system for prostate cancer diagnosis. Urology 68(2), 357–361 (2006)

    Google Scholar 

  19. E.D. Übeyli, Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems. Expert Syst. 27(4), 259–266 (2010)

    Google Scholar 

  20. O.,Geman, I. Chiuchisan, R. Toderean, Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction, in 2017 E-Health and Bioengineering Conference (EHB) (pp. 639–642) (IEEE, 2017)

    Google Scholar 

  21. O. Karan, C. Bayraktar, H. Gümüşkaya, B. Karlık, Diagnosing diabetes using neural networks on small mobile devices. Expert Syst. Appl. 39(1), 54–60 (2012)

    Google Scholar 

  22. E.K.Roy, S.K. Aditya, Prediction of acute myeloid leukemia subtypes based on artificial neural network and adaptive neuro-fuzzy inference system approaches, in Innovations in Electronics and Communication Engineering (pp. 427–439) (Springer, Singapore, 2019)

    Google Scholar 

  23. E. Avci, I. Turkoglu, An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases. Expert Syst. Appl. 36(2), 2873–2878 (2009)

    Google Scholar 

  24. O. Mokhlessi, H.M. Rad, N. Mehrshad, A. Mokhlessi, Application of neural networks in diagnosis of vlave physiological heart disease from heart sounds. Am. J. Biomed. Eng. 1(1), 26–34 (2011)

    Google Scholar 

  25. A. Yadollahpour, J. Nourozi, S.A. Mirbagheri, E. Simancas-Acevedo, F.R. Trejo-Macotela, Designing and implementing an ANFIS based medical decision support system to predict chronic kidney disease progression. Front. Physiol. 9, 1753 (2018)

    Google Scholar 

  26. B. Akdemir, S. Kara, K. Polat, A. Güven, S. Güneş, Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals. Art. Intell. Med. 43(2), 141–149 (2008)

    Google Scholar 

  27. S.M. Odeh, Using an adaptive neuro-fuzzy inference system (AnFis) algorithm for automatic diagnosis of skin cancer. J. Commun. Comput. 8(9), 751–755 (2011)

    Google Scholar 

  28. F. Ghali, Skin cancer diagnosis by using fuzzy logic and GLCM. J. Phys.: Conf. Series 1279(1), 012020. (IOP Publishing, 2019)

    Google Scholar 

  29. W. Muhammad, G.R. Hart, B. Nartowt, J.J. Farrell, K. Johung, Y. Liang, J. Deng, Pancreatic cancer prediction through an artificial neural network. Front. Art. Intell. 2, 2 (2019)

    Google Scholar 

  30. M.U. Sanoob, A. Madhu, K. Ajesh, S.M. Varghese, Artificial neural network for diagnosis of pancreatic cancer. Int. J. Cyber. Infor. (IJCI) 5(2), 41–49 (2016)

    Google Scholar 

  31. D. Zafeiris, S. Rutella, G.R. Ball, An artificial neural network integrated pipeline for biomarker discovery using Alzheimer's disease as a case study. Comput. Struct. Biotech. J. 16, 77–87 (2018)

    Google Scholar 

  32. A. Yiğit, Z. Işik, Application of artificial neural networks in dementia and alzheimer's diagnosis, in 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1–4) (IEEE, 2018)

    Google Scholar 

  33. E. García-Pérez, A. Violante, F. Cervantes-Pérez, Using neural networks for differential diagnosis of Alzheimer disease and vascular dementia. Expert Syst. Appl. 14(1–2), 219–225 (1998)

    Google Scholar 

  34. S.B. Benazir, A. Nagarajan, An innovative system for classifying cervical cancer using features based ANFIS classifier.

    Google Scholar 

  35. M.A.Devi, S. Ravi, J. Vaishnavi, S. Punitha, Classification of cervical cancer using artificial neural networks. Procedia Comput. Sci. 89, 465–472 (2016)

    Google Scholar 

  36. N. Ramamurthy, K.C.T. Swamy, G. Ramarao, M.R. Pasha, Detection of glaucoma using adaptive neuro fuzzy in DWT domain.

    Google Scholar 

  37. N.E.A. Khalid, S. Ibrahim, M. Manaf, Comparative study of adaptive network-based fuzzy inference system (ANFIS), k-nearest neighbors (k-NN) and fuzzy c-means (FCM) for brain abnormalities segmentation. Int. J. Comput. 4.

    Google Scholar 

  38. N.M. Noor, N.E.A. Khalid, R. Hassan, S. Ibrahim, I.M. Yassin, Adaptive neuro-fuzzy inference system for brain abnormality segmentation, in 2010 IEEE Control and System Graduate Research Colloquium (ICSGRC 2010) (pp. 68–70) (IEEE, 2010)

    Google Scholar 

  39. T. Vijayakumar, Classification of brain cancer type using machine learning. J. Art. Intel. 1(02), 105–113 (2019)

    Google Scholar 

  40. T. Karthikeyan, J.S. Kumar, R. Jegan, Design and development of low-cost blood serum analyser using adaptive neuro fuzzy inference system. Int. J. Biomed. Eng. Technol. 15(3), 261–272 (2014)

    Google Scholar 

  41. A. Bani, An adaptive neurofuzzy technique for determination of blood acidity. Comput. Method. Biomech. Biomed. Eng. 13(6), 685–691 (2010)

    Google Scholar 

  42. T. Uçar, A. Karahoca, D. Karahoca, Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets. Neural Comput. Appl. 23(2), 471–483 (2013)

    Google Scholar 

  43. E.P. Noronha, H.T. Marinho, E.B.A.F. Thomaz, C.A. Silva, G.L.R. Veras, R.A.G. Oliveira, Immunophenotypic characterization of acute leukemia at a public oncology reference center in Maranhão, northeastern Brazil. Sao Paulo Med. J. 129(6), 392–401 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Bangladesh Atomic Energy Commission, Dhaka, Bangladesh

    Md. Ziaul Hasan Majumder & Md. Abu Khaer

  2. Jahangirnagar University, Dhaka, Bangladesh

    Md. Julkar Nayeen Mahi

  3. Changchun University of Science and Technology, Jilin, China

    Md. Shaiful Islam Babu

  4. University of Dhaka, Dhaka, Bangladesh

    Subrata Kumar Aditya

Authors
  1. Md. Ziaul Hasan Majumder
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  2. Md. Abu Khaer
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  3. Md. Julkar Nayeen Mahi
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  4. Md. Shaiful Islam Babu
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  5. Subrata Kumar Aditya
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Editor information

Editors and Affiliations

  1. Research and Industry Incubation Center, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

    V. Suma

  2. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  3. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

  4. GoPerception Laboratory, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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Ziaul Hasan Majumder, M., Abu Khaer, M., Nayeen Mahi, M.J., Shaiful Islam Babu, M., Aditya, S.K. (2021). Decision Support Technique for Prediction of Acute Lymphoblastic Leukemia Subtypes Based on Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_40

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