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Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology
Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable diseas...
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/PMC9525194/ https://www.ncbi.nlm.nih.gov/pubmed/36188697 http://dx.doi.org/10.1155/2022/3373553 |
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author | Hong, Zhen Xu, Qin Yan, Xin Zhang, Ran Ren, Yuanfang Tong, Qian |
author_facet | Hong, Zhen Xu, Qin Yan, Xin Zhang, Ran Ren, Yuanfang Tong, Qian |
author_sort | Hong, Zhen |
collection | PubMed |
description | Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since “early identification is the way to avoidance in any malignant growth,” the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, “Early discovery is the way to counteraction in the event of any malignant growth.” |
format | Online Article Text |
id | pubmed-9525194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95251942022-10-01 Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology Hong, Zhen Xu, Qin Yan, Xin Zhang, Ran Ren, Yuanfang Tong, Qian Comput Intell Neurosci Research Article Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since “early identification is the way to avoidance in any malignant growth,” the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, “Early discovery is the way to counteraction in the event of any malignant growth.” Hindawi 2022-09-23 /pmc/articles/PMC9525194/ /pubmed/36188697 http://dx.doi.org/10.1155/2022/3373553 Text en Copyright © 2022 Zhen Hong 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 Hong, Zhen Xu, Qin Yan, Xin Zhang, Ran Ren, Yuanfang Tong, Qian Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology |
title | Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology |
title_full | Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology |
title_fullStr | Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology |
title_full_unstemmed | Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology |
title_short | Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology |
title_sort | analysis of signs and effects of surgical breast cancer patients based on big data technology |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525194/ https://www.ncbi.nlm.nih.gov/pubmed/36188697 http://dx.doi.org/10.1155/2022/3373553 |
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