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Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions
The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the s...
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/PMC9526607/ https://www.ncbi.nlm.nih.gov/pubmed/36193309 http://dx.doi.org/10.1155/2022/7352489 |
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author | Chen, Wenao Zeng, Ruijie Jin, Yiyao Sun, Xi Zhou, Zihan Zhu, Chao |
author_facet | Chen, Wenao Zeng, Ruijie Jin, Yiyao Sun, Xi Zhou, Zihan Zhu, Chao |
author_sort | Chen, Wenao |
collection | PubMed |
description | The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When N = 2, the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved. |
format | Online Article Text |
id | pubmed-9526607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95266072022-10-02 Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions Chen, Wenao Zeng, Ruijie Jin, Yiyao Sun, Xi Zhou, Zihan Zhu, Chao Biomed Res Int Research Article The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When N = 2, the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved. Hindawi 2022-09-22 /pmc/articles/PMC9526607/ /pubmed/36193309 http://dx.doi.org/10.1155/2022/7352489 Text en Copyright © 2022 Wenao Chen 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 Chen, Wenao Zeng, Ruijie Jin, Yiyao Sun, Xi Zhou, Zihan Zhu, Chao Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions |
title | Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions |
title_full | Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions |
title_fullStr | Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions |
title_full_unstemmed | Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions |
title_short | Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions |
title_sort | artificial neural network assisted cancer risk prediction of oral precancerous lesions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526607/ https://www.ncbi.nlm.nih.gov/pubmed/36193309 http://dx.doi.org/10.1155/2022/7352489 |
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