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Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282979/ https://www.ncbi.nlm.nih.gov/pubmed/35845866 http://dx.doi.org/10.1155/2022/6715406 |
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author | Bhardwaj, Arpit Bhardwaj, Harshit Sakalle, Aditi Uddin, Ziya Sakalle, Maneesha Ibrahim, Wubshet |
author_facet | Bhardwaj, Arpit Bhardwaj, Harshit Sakalle, Aditi Uddin, Ziya Sakalle, Maneesha Ibrahim, Wubshet |
author_sort | Bhardwaj, Arpit |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9282979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92829792022-07-15 Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification Bhardwaj, Arpit Bhardwaj, Harshit Sakalle, Aditi Uddin, Ziya Sakalle, Maneesha Ibrahim, Wubshet Comput Intell Neurosci Research Article 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. Hindawi 2022-07-07 /pmc/articles/PMC9282979/ /pubmed/35845866 http://dx.doi.org/10.1155/2022/6715406 Text en Copyright © 2022 Arpit Bhardwaj et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Bhardwaj, Arpit Bhardwaj, Harshit Sakalle, Aditi Uddin, Ziya Sakalle, Maneesha Ibrahim, Wubshet Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification |
title | Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification |
title_full | Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification |
title_fullStr | Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification |
title_full_unstemmed | Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification |
title_short | Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification |
title_sort | tree-based and machine learning algorithm analysis for breast cancer classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282979/ https://www.ncbi.nlm.nih.gov/pubmed/35845866 http://dx.doi.org/10.1155/2022/6715406 |
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