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An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning

Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further t...

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Autores principales: Ma, Zezhong, Zhang, Meng, Liu, Jiajia, Yang, Aimin, Li, Hao, Wang, Jian, Hua, Dianbo, Li, Mingduo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928102/
https://www.ncbi.nlm.nih.gov/pubmed/35311106
http://dx.doi.org/10.3389/fonc.2022.860532
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author Ma, Zezhong
Zhang, Meng
Liu, Jiajia
Yang, Aimin
Li, Hao
Wang, Jian
Hua, Dianbo
Li, Mingduo
author_facet Ma, Zezhong
Zhang, Meng
Liu, Jiajia
Yang, Aimin
Li, Hao
Wang, Jian
Hua, Dianbo
Li, Mingduo
author_sort Ma, Zezhong
collection PubMed
description Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further treatment of cancer patients. According to statistics, about 90% of cancer-related deaths are due to metastasis of primary tumor cells. Therefore, the study of the location of cancer recurrence and its influencing factors is of great significance for the clinical diagnosis and treatment of cancer. In this paper, we propose an assisted diagnosis model for cancer patients based on federated learning. In terms of data, the influencing factors of cancer recurrence and the special needs of data samples required by federated learning were comprehensively considered. Six first-level impact indicators were determined, and the historical case data of cancer patients were further collected. Based on the federated learning framework combined with convolutional neural network, various physical examination indicators of patients were taken as input. The recurrence time and recurrence location of patients were used as output to construct an auxiliary diagnostic model, and linear regression, support vector regression, Bayesling regression, gradient ascending tree and multilayer perceptrons neural network algorithm were used as comparison algorithms. CNN’s federated prediction model based on improved under the condition of the joint modeling and simulation on the five types of cancer data accuracy reached more than 90%, the accuracy is better than single modeling machine learning tree model and linear model and neural network, the results show that auxiliary diagnosis model based on the study of cancer patients in assisted the doctor in the diagnosis of patients, As well as effectively provide nutritional programs for patients and have application value in prolonging the life of patients, it has certain guiding significance in the field of medical cancer rehabilitation.
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spelling pubmed-89281022022-03-18 An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning Ma, Zezhong Zhang, Meng Liu, Jiajia Yang, Aimin Li, Hao Wang, Jian Hua, Dianbo Li, Mingduo Front Oncol Oncology Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further treatment of cancer patients. According to statistics, about 90% of cancer-related deaths are due to metastasis of primary tumor cells. Therefore, the study of the location of cancer recurrence and its influencing factors is of great significance for the clinical diagnosis and treatment of cancer. In this paper, we propose an assisted diagnosis model for cancer patients based on federated learning. In terms of data, the influencing factors of cancer recurrence and the special needs of data samples required by federated learning were comprehensively considered. Six first-level impact indicators were determined, and the historical case data of cancer patients were further collected. Based on the federated learning framework combined with convolutional neural network, various physical examination indicators of patients were taken as input. The recurrence time and recurrence location of patients were used as output to construct an auxiliary diagnostic model, and linear regression, support vector regression, Bayesling regression, gradient ascending tree and multilayer perceptrons neural network algorithm were used as comparison algorithms. CNN’s federated prediction model based on improved under the condition of the joint modeling and simulation on the five types of cancer data accuracy reached more than 90%, the accuracy is better than single modeling machine learning tree model and linear model and neural network, the results show that auxiliary diagnosis model based on the study of cancer patients in assisted the doctor in the diagnosis of patients, As well as effectively provide nutritional programs for patients and have application value in prolonging the life of patients, it has certain guiding significance in the field of medical cancer rehabilitation. Frontiers Media S.A. 2022-03-03 /pmc/articles/PMC8928102/ /pubmed/35311106 http://dx.doi.org/10.3389/fonc.2022.860532 Text en Copyright © 2022 Ma, Zhang, Liu, Yang, Li, Wang, Hua and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Ma, Zezhong
Zhang, Meng
Liu, Jiajia
Yang, Aimin
Li, Hao
Wang, Jian
Hua, Dianbo
Li, Mingduo
An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
title An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
title_full An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
title_fullStr An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
title_full_unstemmed An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
title_short An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
title_sort assisted diagnosis model for cancer patients based on federated learning
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928102/
https://www.ncbi.nlm.nih.gov/pubmed/35311106
http://dx.doi.org/10.3389/fonc.2022.860532
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