Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm
Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients’ survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algor...
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| Vydáno v: | Computational and mathematical methods in medicine Ročník 2022; s. 1 - 14 |
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| Jazyk: | angličtina |
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08.07.2022
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| ISSN: | 1748-670X, 1748-6718, 1748-6718 |
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| Abstract | Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients’ survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC. |
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| AbstractList | Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients’ survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC. Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC.Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC. Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients’ survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC. |
| Author | Wang, Yanfeng Zhang, Wenhao Wang, Lidong Song, Xin Zhao, Xueke Sun, Junwei |
| AuthorAffiliation | 2 State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450066, China 1 School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China |
| AuthorAffiliation_xml | – name: 2 State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450066, China – name: 1 School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China |
| Author_xml | – sequence: 1 givenname: Yanfeng surname: Wang fullname: Wang, Yanfeng organization: School of Electrical and Information EngineeringZhengzhou University of Light IndustryZhengzhou 450000Chinazzuli.edu.cn – sequence: 2 givenname: Wenhao orcidid: 0000-0003-3917-5599 surname: Zhang fullname: Zhang, Wenhao organization: School of Electrical and Information EngineeringZhengzhou University of Light IndustryZhengzhou 450000Chinazzuli.edu.cn – sequence: 3 givenname: Junwei orcidid: 0000-0001-8518-5064 surname: Sun fullname: Sun, Junwei organization: School of Electrical and Information EngineeringZhengzhou University of Light IndustryZhengzhou 450000Chinazzuli.edu.cn – sequence: 4 givenname: Lidong surname: Wang fullname: Wang, Lidong organization: State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated HospitalZhengzhou UniversityZhengzhou 450066Chinazzu.edu.cn – sequence: 5 givenname: Xin surname: Song fullname: Song, Xin organization: State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated HospitalZhengzhou UniversityZhengzhou 450066Chinazzu.edu.cn – sequence: 6 givenname: Xueke surname: Zhao fullname: Zhao, Xueke organization: State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated HospitalZhengzhou UniversityZhengzhou 450066Chinazzu.edu.cn |
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| Cites_doi | 10.1053/j.gastro.2019.11.030 10.1016/j.athoracsur.2019.09.028 10.1016/j.asjsur.2016.10.005 10.1126/science.aau3879 10.1200/JCO.19.02503 10.1007/s11604-018-0726-3 10.1504/IJAPR.2021.117203 10.4251/wjgo.v6.i5.112 10.1007/s00520-020-05483-0 10.3389/fgene.2020.615864 10.1002/ijc.33588 10.1016/j.radonc.2019.07.006 10.1109/ACCESS.2021.3108533 10.6004/jnccn.2019.0033 10.1097/SLA.0000000000003772 10.3389/fonc.2021.644860 10.1162/neco.2006.18.7.1527 10.1109/MSP.2017.2738401 10.1007/s00500-019-03856-0 10.1016/j.cmpb.2017.09.005 10.1142/S0218127418501766 10.1504/IJBIC.2021.118101 10.1088/0957-0233/26/11/115002 10.1016/S0140-6736(21)00001-5 10.2174/1574893614666190902152142 10.1109/TSP.2022.3173154 10.3390/cancers13010141 10.1001/jamanetworkopen.2020.32269 10.1007/s10388-021-00826-0 10.1007/s10462-019-09732-5 10.1504/IJBIC.2021.114875 10.1097/SLA.0000000000002985 10.1109/TBCAS.2021.3090786 10.1080/21642583.2019.1708830 10.1016/j.measurement.2021.110079 10.1001/jamaoncol.2020.7478 10.1097/CM9.0000000000001474 10.1016/j.dibe.2021.100045 10.1016/j.gie.2019.12.049 10.1056/NEJMoa2032125 10.1053/j.gastro.2019.10.039 10.1002/jnm.2829 10.1007/s10489-020-01893-z 10.1109/TCYB.2019.2951520 10.1080/1062936X.2018.1491414 10.1097/SLA.0000000000003445 10.1097/SLA.0000000000003500 10.1016/j.pdpdt.2020.102104 |
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| Title | Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm |
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