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An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries
Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448109/ https://www.ncbi.nlm.nih.gov/pubmed/32879636 http://dx.doi.org/10.1155/2020/5363549 |
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author | Zhang, Jun Chen, Zhigang Wu, Jia Liu, Kanghuai |
author_facet | Zhang, Jun Chen, Zhigang Wu, Jia Liu, Kanghuai |
author_sort | Zhang, Jun |
collection | PubMed |
description | Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively. |
format | Online Article Text |
id | pubmed-7448109 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-74481092020-09-01 An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries Zhang, Jun Chen, Zhigang Wu, Jia Liu, Kanghuai Comput Math Methods Med Research Article Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively. Hindawi 2020-08-17 /pmc/articles/PMC7448109/ /pubmed/32879636 http://dx.doi.org/10.1155/2020/5363549 Text en Copyright © 2020 Jun Zhang et al. http://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 Zhang, Jun Chen, Zhigang Wu, Jia Liu, Kanghuai An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries |
title | An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries |
title_full | An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries |
title_fullStr | An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries |
title_full_unstemmed | An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries |
title_short | An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries |
title_sort | intelligent decision-making support system for the detection and staging of prostate cancer in developing countries |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448109/ https://www.ncbi.nlm.nih.gov/pubmed/32879636 http://dx.doi.org/10.1155/2020/5363549 |
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