A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms

Fatma Akalın; Mehmet Fatih Orhan; Mustafa Buyukavci

doi:10.35377/saucis...1210786

EN

A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms

Abstract

Hodgkin-type lymphoma is a disease with unique histological, immunophenotypic, and clinical features. This disease occurs in nearly 30% of all lymphomas. Its treatable is high. However, the treatment plan is specified after the stage and risk status are determined. For this reason, it is an important process for doctors to decide on the stage of the disease correctly. Some of the data used for this decision are the patient's history, detailed physical examination, laboratory findings, imaging methods and bone marrow biopsy results. Hybrid FDG-PET is the other method used in the medical world. This method is used in diagnosis, evaluation of response given to treatment, staging and restaging process. However, it is radiation-based. Therefore it has the possibility of producing undesirable results in the future. In this study, an artificial intelligence-based computer-assisted decision support system is done to reduce the number of used medical methods and radiation exposure. Data were obtained from the NCBI-GEO dataset. The evaluation of these data, which contains missing values, is handled in two ways. Firstly, samples with missing values in the initial evaluation are deleted from the dataset. Then, these data are trained with “trainlm” function in artificial neural network architecture. However, reducing the error value of the estimates is important. For this, the artificial neural network architecture is retrained with the artificial bee colony algorithm, particle swarm optimization algorithm and invasive weed algorithm, respectively. Secondly, the same operations are performed again on the dataset containing missing values. As a result of the training, the maximum performance was obtained for invasive weed and particle swarm optimization algorithms with 1,45547E+14 and 1,23103E+14 average error rates, respectively.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence , Software Engineering (Other)

Journal Section

Research Article

Authors

Fatma Akalın ^*
0000-0001-6670-915X
Türkiye

Mehmet Fatih Orhan
0000-0001-8081-6760
Türkiye

Mustafa Buyukavci
0000-0002-9054-3134
Türkiye

Publication Date

December 31, 2022

Submission Date

November 27, 2022

Acceptance Date

December 6, 2022

Published in Issue

Year 2022 Volume: 5 Number: 3

DOI

https://doi.org/10.35377/saucis...1210786

IZ

https://izlik.org/JA53PJ57BP

Cite

RIS / Bibtex

APA

Akalın, F., Orhan, M. F., & Buyukavci, M. (2022). A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms. Sakarya University Journal of Computer and Information Sciences, 5(3), 448-461. https://doi.org/10.35377/saucis...1210786

AMA

1.Akalın F, Orhan MF, Buyukavci M. A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms. SAUCIS. 2022;5(3):448-461. doi:10.35377/saucis.1210786

Chicago

Akalın, Fatma, Mehmet Fatih Orhan, and Mustafa Buyukavci. 2022. “A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms”. Sakarya University Journal of Computer and Information Sciences 5 (3): 448-61. https://doi.org/10.35377/saucis. 1210786.

EndNote

Akalın F, Orhan MF, Buyukavci M (December 1, 2022) A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms. Sakarya University Journal of Computer and Information Sciences 5 3 448–461.

IEEE

[1]F. Akalın, M. F. Orhan, and M. Buyukavci, “A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms”, SAUCIS, vol. 5, no. 3, pp. 448–461, Dec. 2022, doi: 10.35377/saucis...1210786.

ISNAD

Akalın, Fatma - Orhan, Mehmet Fatih - Buyukavci, Mustafa. “A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms”. Sakarya University Journal of Computer and Information Sciences 5/3 (December 1, 2022): 448-461. https://doi.org/10.35377/saucis. 1210786.

JAMA

1.Akalın F, Orhan MF, Buyukavci M. A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms. SAUCIS. 2022;5:448–461.

MLA

Akalın, Fatma, et al. “A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 3, Dec. 2022, pp. 448-61, doi:10.35377/saucis. 1210786.

Vancouver

1.Fatma Akalın, Mehmet Fatih Orhan, Mustafa Buyukavci. A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms. SAUCIS. 2022 Dec. 1;5(3):448-61. doi:10.35377/saucis. 1210786