Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification-Reference-Cited by-同舟云学术

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

Published:2022-07-07 Issue: Volume:2022 Page:1-6
ISSN:1687-5273
Container-title:Computational Intelligence and Neuroscience
language:en
Short-container-title:Computational Intelligence and Neuroscience

Author:

Bhardwaj Arpit¹^ORCID,Bhardwaj Harshit²^ORCID,Sakalle Aditi³^ORCID,Uddin Ziya⁴^ORCID,Sakalle Maneesha⁵^ORCID,Ibrahim Wubshet⁶^ORCID

Affiliation:

1. Department of Computer Science and Engineering, BML Munjal University, Kapriwas, Gurugram, Haryana, India

2. Department of Computer Science and Engineering, Galgotias University, Greater Noida, India

3. Department of Computer Science and Engineering, University School of Information and Communication Technology, Gautam Buddha University, Greater Noida, India

4. Department of Applied Sciences, SoEt, BML Munjal University, Kapriwas, Gurugram, Haryana, India

5. Department of Mathematics, Govt. S. N. P. G. College, Khandwa, India

6. Department of Mathematics, Ambo University, Ambo, Ethiopia

Abstract

Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Link

http://downloads.hindawi.com/journals/cin/2022/6715406.pdf

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