A Comparative Study between a Simulation of Machine Learning and Extreme Learning Machine Techniques on Breast Cancer Diagnosis

Rahul Reddy Nadikattu

A Comparative Study between a Simulation of Machine Learning and Extreme Learning Machine Techniques on Breast Cancer Diagnosis

Rahul Reddy Nadikattu * § Ph.D. Student

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University of the Cumberlands

A Comparative Study between a Simulation of Machine Learning and Extreme Learning Machine Techniques on Breast Cancer Diagnosis

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Abstract

Breast Cancer is a developing and most normal disease among ladies around the globe. Breast malignancy is an uncontrolled and exorbitant development of abnormal cells in the Breast because of hereditary, hormonal, and way of life factors. During the starting stages, the tumor is restricted to the Breast, and in the latter part, it can spread to lymph hubs in the armpit and different organs like the liver, bones, lungs, and cerebrum. At the point when the bosom disease spreads to different pieces of the body, it is going to metastasize. The sickness is repairable in the beginning periods, yet it is identified in later stages, which is the fundamental driver for the passing of such a large number of ladies in this entire world. Clinical tests led in medical clinics for deciding the malady are a lot of costly, just as tedious as well.

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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

Rahul Reddy Nadikattu. 2020. "A Comparative Study between a Simulation of Machine Learning and Extreme Learning Machine Techniques on Breast Cancer Diagnosis". Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 20 (GJCST Volume 20 Issue H1).

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GJCST Volume 20 Issue H1

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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-H Classification I.2.m

Submission ReceivedDecember 12, 2019
Peer Review Double Blind
Handling Editor
Accepted January 4, 2020
Published January 15, 2020

Version of record

v1.2

Issue date

October 16, 2020

Language

English

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