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Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural network

  • P.Mohamed ShakeelEmail author
  • Gunasekaran Manogaran
Original Paper
  • 16 Downloads
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health

Abstract

Prostate cancer is commonly occurs in prostate that affects small walnut and generates the seminal fluid for men. This disease is happening due to urinating trouble, blood semen, bone pain, stream of urine other harmful activities such as race, obesity and genetic changes. The improper symptoms of prostate cancer disease, it is challenge to identify it in the starting stage. So, different soft computing and machine learning techniques utilized to predict the Prostate cancer due to its severe side effects. Initially prostate cancer biomedical information has been collected from DBCR dataset that manage the patient age, cancer volume, prostate weight, Gleason score, vesicle invasion, prostate specific antigen details and so on. In the wake of gathering prostate biomedical data, undesirable information has been evacuated by applying the mean mode based standardization procedures and the advanced elements are chosen with the assistance of the subterranean insect harsh set hypothesis. The chose information has been arranged utilizing the outspread prepared extraordinary learning neural systems. The classifier successfully classifies the abnormal prostate features. At that point the effectiveness of prostate cancer prediction framework is inspected using assistance of mean square error rate, hit rate, selectivity and accuracy.

Keywords

Prostate cancer Clinical attachment level Means mode based standardization Neural networks 

Notes

Compliance with ethical standards

Conflicts of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Bourdoumis A, Papatsoris AG, Chrisofos M, Efstathiou E, Skolarikos A, Deliveliotis C. The novel prostate cancer antigen 3 (PCA3) biomarker. Int Braz J Urol. 2010;36(6):665–8; discussion 669.  https://doi.org/10.1590/S1677.CrossRefGoogle Scholar
  2. 2.
    Rendon RA, Mason RJ, Marzouk K, Finelli A, Saad F, So A, et al. Canadian Urological Association recommendations on prostate cancer screening and early diagnosis. Can Urol Assoc J. 2017;11(10):298–309.  https://doi.org/10.5489/cuaj.4888 ISSN 1920-1214.CrossRefGoogle Scholar
  3. 3.
    Alberts AR, Schoots IG, Roobol MJ. Prostate-specific antigen-based prostate cancer screening: past and future. Int J Urol. 2015;22(6):524–32.  https://doi.org/10.1111/iju.12750.CrossRefGoogle Scholar
  4. 4.
    Rowles JL, Ranard KM, Applegate CC, Jeon S, An R, Erdman JW. Processed and raw tomato consumption and risk of prostate cancer: a systematic review and dose–response meta-analysis. Prostate Cancer Prostatic Dis. 2018.  https://doi.org/10.1038/s41391-017-0005-x ISSN 1476–5608.
  5. 5.
    Qaseem A, Barry MJ, Denberg TD, Owens DK, Shekelle P. Screening for prostate Cancer: a guidance statement from the clinical guidelines Committee of the American College of physicians. Ann Intern Med. 2013;158(10):761–9.  https://doi.org/10.7326/0003-4819-158-10-201305210-00633.CrossRefGoogle Scholar
  6. 6.
    Mohand Yaghi Kehinde EO. Oral antibiotics in trans-rectal prostate biopsy and its efficacy to reduce infectious complications: systematic review. Urol Ann. 2015;7(4):417–27.  https://doi.org/10.4103/0974-7796.164860.CrossRefGoogle Scholar
  7. 7.
    Reda I, Khalil A, Elmogy M, El-Fetouh AA, Shalaby A, El-Ghar MA, et al. Deep learning role in early diagnosis of prostate cancer. Technol Cancer Res Treat. 2018;17:1533034618775530.CrossRefGoogle Scholar
  8. 8.
    Kumara N, Vermaa R, Aroraa A, Kumara A, Guptaa S, Sethia A, Gann PH. Convolutional Neural Networks for Prostate Cancer Recurrence Prediction. http://www.iitg.ac.in/amitsethi/publications/17.02%20PCaRec%20SPIE.pdf.
  9. 9.
    Wichard JD, Cammann H, Stephan C, Tolxdorff T. Classification models for early detection of prostate Cancer. J Biomed Biotechnol. 2008;2008:218097.CrossRefGoogle Scholar
  10. 10.
    Takeuchi T, Hattori-Kato M, Okuno Y, Iwai S, Mikami K. Prediction of prostate cancer by deep learning with multilayer artificial neural Network. https://www.biorxiv.org/content/early/2018/03/29/291609.
  11. 11.
    Zlotta AR, Remzi M, Snow PB, Schulman CC, Marberger M, Djavan B. An artificial neural network for prostate cancer staging when serum prostate specific antigen is 10 ng./ml. Or less. J Urol. 2003;169(5):1724–8.CrossRefGoogle Scholar
  12. 12.
    Sridhar KP, Baskar S, Shakeel PM, et al. Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Human Comput. 2018.  https://doi.org/10.1007/s12652-018-1058-y.
  13. 13.
    Pandey KK, Pradhan N. An analytical and comparative study of various data preprocessing method in data mining. Int J Emerg Technol Adv Eng 2014: 4(10).Google Scholar
  14. 14.
    Sahua B, Mishrab D. A novel feature selection algorithm using particle swarm optimization for Cancer microarray data. Int Conf Model Optim Comput. 2012;38:27–31.Google Scholar
  15. 15.
    Hasan MM, Mishra PK. Robust gesture recognition using Gaussian distribution for features fitting. Int J Mach Learn Comput. 2012; 2(3).Google Scholar
  16. 16.
    Kaur H, Kaur L. Performance comparison of different feature detection methods with Gabor filter. Int J Sci Res (IJSR). 2014;3(5):1880–6.Google Scholar
  17. 17.
    Donnelley M, Knowles G. Computer aided long bone fracture detection. IEEE; 175–178.Google Scholar
  18. 18.
    Karring, edited by Jan Lindhe, Niklaus P. Lang, Thorkild. Clinical periodontology and Implant Dent (5th ed.). Oxford: Blackwell Munksgaard. 2008; 413, 459. ISBN 9781405160995.Google Scholar
  19. 19.
    Singh D, et al. Gene expression correlates of clinical prostate Cancer behavior. Cancer Cell. 2002;1:203–9.CrossRefGoogle Scholar
  20. 20.
    Mohamed Shakeel P, Baskar S, Sarma Dhulipala VR, Mishra S, Jaber MM. Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst. 2018;42:186.CrossRefGoogle Scholar
  21. 21.
    Vályi P, Gorzó I. Periodontal abscess: etiology, diagnosis and treatment. Fogorvosi szemle. 2004;97(4):151–5.Google Scholar
  22. 22.
    Van Der Velden U. Purpose and problems of periodontal disease classification. Periodontology. 2005;2000:39.1.Google Scholar
  23. 23.
    Papantonopoulos G, et al. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One. 2014;9.3:e89757.CrossRefGoogle Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Information and Communication TechnologyUniversiti Teknikal MalaysiaMelakaMalaysia
  2. 2.University of CaliforniaDavisUSA

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