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An integrative machine learning framework for classifying SEER breast cancer
Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067827/ https://www.ncbi.nlm.nih.gov/pubmed/37005484 http://dx.doi.org/10.1038/s41598-023-32029-1 |
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author | Manikandan, P. Durga, U. Ponnuraja, C. |
author_facet | Manikandan, P. Durga, U. Ponnuraja, C. |
author_sort | Manikandan, P. |
collection | PubMed |
description | Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset. |
format | Online Article Text |
id | pubmed-10067827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100678272023-04-04 An integrative machine learning framework for classifying SEER breast cancer Manikandan, P. Durga, U. Ponnuraja, C. Sci Rep Article Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset. Nature Publishing Group UK 2023-04-01 /pmc/articles/PMC10067827/ /pubmed/37005484 http://dx.doi.org/10.1038/s41598-023-32029-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Manikandan, P. Durga, U. Ponnuraja, C. An integrative machine learning framework for classifying SEER breast cancer |
title | An integrative machine learning framework for classifying SEER breast cancer |
title_full | An integrative machine learning framework for classifying SEER breast cancer |
title_fullStr | An integrative machine learning framework for classifying SEER breast cancer |
title_full_unstemmed | An integrative machine learning framework for classifying SEER breast cancer |
title_short | An integrative machine learning framework for classifying SEER breast cancer |
title_sort | integrative machine learning framework for classifying seer breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067827/ https://www.ncbi.nlm.nih.gov/pubmed/37005484 http://dx.doi.org/10.1038/s41598-023-32029-1 |
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