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Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we pr...

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Autores principales: Rasool, Abdur, Bunterngchit, Chayut, Tiejian, Luo, Islam, Md. Ruhul, Qu, Qiang, Jiang, Qingshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949437/
https://www.ncbi.nlm.nih.gov/pubmed/35328897
http://dx.doi.org/10.3390/ijerph19063211
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author Rasool, Abdur
Bunterngchit, Chayut
Tiejian, Luo
Islam, Md. Ruhul
Qu, Qiang
Jiang, Qingshan
author_facet Rasool, Abdur
Bunterngchit, Chayut
Tiejian, Luo
Islam, Md. Ruhul
Qu, Qiang
Jiang, Qingshan
author_sort Rasool, Abdur
collection PubMed
description Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.
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spelling pubmed-89494372022-03-26 Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis Rasool, Abdur Bunterngchit, Chayut Tiejian, Luo Islam, Md. Ruhul Qu, Qiang Jiang, Qingshan Int J Environ Res Public Health Article Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors. MDPI 2022-03-09 /pmc/articles/PMC8949437/ /pubmed/35328897 http://dx.doi.org/10.3390/ijerph19063211 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rasool, Abdur
Bunterngchit, Chayut
Tiejian, Luo
Islam, Md. Ruhul
Qu, Qiang
Jiang, Qingshan
Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
title Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
title_full Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
title_fullStr Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
title_full_unstemmed Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
title_short Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis
title_sort improved machine learning-based predictive models for breast cancer diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949437/
https://www.ncbi.nlm.nih.gov/pubmed/35328897
http://dx.doi.org/10.3390/ijerph19063211
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