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Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approa...

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Autores principales: Haque, Mohammad Nazmul, Tazin, Tahia, Khan, Mohammad Monirujjaman, Faisal, Shahla, Ibraheem, Sobhee Md., Algethami, Haneen, Almalki, Faris A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060999/
https://www.ncbi.nlm.nih.gov/pubmed/35509861
http://dx.doi.org/10.1155/2022/1249692
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author Haque, Mohammad Nazmul
Tazin, Tahia
Khan, Mohammad Monirujjaman
Faisal, Shahla
Ibraheem, Sobhee Md.
Algethami, Haneen
Almalki, Faris A.
author_facet Haque, Mohammad Nazmul
Tazin, Tahia
Khan, Mohammad Monirujjaman
Faisal, Shahla
Ibraheem, Sobhee Md.
Algethami, Haneen
Almalki, Faris A.
author_sort Haque, Mohammad Nazmul
collection PubMed
description Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.
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spelling pubmed-90609992022-05-03 Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches Haque, Mohammad Nazmul Tazin, Tahia Khan, Mohammad Monirujjaman Faisal, Shahla Ibraheem, Sobhee Md. Algethami, Haneen Almalki, Faris A. Comput Math Methods Med Research Article Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area. Hindawi 2022-04-25 /pmc/articles/PMC9060999/ /pubmed/35509861 http://dx.doi.org/10.1155/2022/1249692 Text en Copyright © 2022 Mohammad Nazmul Haque 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
Haque, Mohammad Nazmul
Tazin, Tahia
Khan, Mohammad Monirujjaman
Faisal, Shahla
Ibraheem, Sobhee Md.
Algethami, Haneen
Almalki, Faris A.
Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
title Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
title_full Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
title_fullStr Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
title_full_unstemmed Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
title_short Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
title_sort predicting characteristics associated with breast cancer survival using multiple machine learning approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060999/
https://www.ncbi.nlm.nih.gov/pubmed/35509861
http://dx.doi.org/10.1155/2022/1249692
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