Diagnosis of Prostate Cancer using Soft Computing Paradigms

Samuel S. Udoh; Uduak A. Umoh; Michael E. Umoh; Mfon E. Udo

Diagnosis of Prostate Cancer using Soft Computing Paradigms

Samuel S. Udoh * §

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Uduak A. Umoh

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Michael E. Umoh

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Mfon E. Udo

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Federal University of Technology

Diagnosis of Prostate Cancer using Soft Computing Paradigms

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Abstract

The process of diagnosing of prostate cancer using traditional methods is cumbersome because of the similarity of symptoms that are present in other diseases. Soft Computing (SC) paradigms which mimic human imprecise data manipulation and learning capabilities have been reviewed and harnessed for diagnosis and classification of prostate cancer. SC technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS) facilitated symptoms analysis, diagnosis and prostate cancer classification. Age of Patient (AP), Pains in Urination (PU), Frequent Urination (FU), Blood in Semen (BS) and Pains in Pelvic (PP) served as input attributes while Prostate Risk (PR) served as output. Matrix laboratory provided the programming tools for system implementation. The practical function of the system was assessed using prostate cancer data collected from the University of Uyo Teaching Hospital. A 95% harmony observed between the computed and the expected output in the ANFIS model, showed the superiority of the ANFIS model over the fuzzy model. The system is poised to assist medical professionals in the domain of diagnosis and classification of prostate cancer for the promotion of management and treatment decisions.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Samuel S. Udoh, Uduak A. Umoh, Michael E. Umoh, Mfon E. Udo. 2019. "Diagnosis of Prostate Cancer using Soft Computing Paradigms". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D2).

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GJCST Volume 19 Issue D2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification F.1.1

Submission ReceivedDecember 7, 2018
Peer Review Double Blind
Handling Editor
Accepted January 4, 2019
Published January 15, 2019

Version of record

v1.2

Issue date

May 18, 2019

Language

English

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Article Matrices

Total Score: 144

Country: Nigeria

Subject: Global Journal of Computer Science and Technology

Authors: Samuel S. Udoh, Uduak A. Umoh, Michael E. Umoh, Mfon E. Udo (PhD/Dr. count: 0)

View Count (all-time): 405

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Publish Date: 2019 01, Tue

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