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
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health


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.


Prostate cancer Clinical attachment level Means mode based standardization Neural networks 


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.


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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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