Diagnosing Hematological Disorders Using Deep Learning Method

Tuba Karagül; Nilüfer Yurtay; Birgül Öneç

doi:10.35377/saucis.04.02.836375

EN

Diagnosing Hematological Disorders Using Deep Learning Method

Abstract

Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

Keywords

Supporting Institution

Research Fund of Sakarya University, Turkey

Project Number

2015-50-02-010

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Tuba Karagül ^*
0000-0002-9382-2606
Türkiye

Nilüfer Yurtay
0000-0002-7577-7506
Türkiye

Birgül Öneç
0000-0003-2824-1044
Türkiye

Publication Date

August 31, 2021

Submission Date

December 5, 2020

Acceptance Date

July 19, 2021

Published in Issue

Year 2021 Volume: 4 Number: 2

DOI

https://doi.org/10.35377/saucis.04.02.836375

IZ

https://izlik.org/JA95SM63CB

Cite

RIS / Bibtex

APA

Karagül, T., Yurtay, N., & Öneç, B. (2021). Diagnosing Hematological Disorders Using Deep Learning Method. Sakarya University Journal of Computer and Information Sciences, 4(2), 227-243. https://doi.org/10.35377/saucis.04.02.836375

AMA

1.Karagül T, Yurtay N, Öneç B. Diagnosing Hematological Disorders Using Deep Learning Method. SAUCIS. 2021;4(2):227-243. doi:10.35377/saucis.04.02.836375

Chicago

Karagül, Tuba, Nilüfer Yurtay, and Birgül Öneç. 2021. “Diagnosing Hematological Disorders Using Deep Learning Method”. Sakarya University Journal of Computer and Information Sciences 4 (2): 227-43. https://doi.org/10.35377/saucis.04.02.836375.

EndNote

Karagül T, Yurtay N, Öneç B (August 1, 2021) Diagnosing Hematological Disorders Using Deep Learning Method. Sakarya University Journal of Computer and Information Sciences 4 2 227–243.

IEEE

[1]T. Karagül, N. Yurtay, and B. Öneç, “Diagnosing Hematological Disorders Using Deep Learning Method”, SAUCIS, vol. 4, no. 2, pp. 227–243, Aug. 2021, doi: 10.35377/saucis.04.02.836375.

ISNAD

Karagül, Tuba - Yurtay, Nilüfer - Öneç, Birgül. “Diagnosing Hematological Disorders Using Deep Learning Method”. Sakarya University Journal of Computer and Information Sciences 4/2 (August 1, 2021): 227-243. https://doi.org/10.35377/saucis.04.02.836375.

JAMA

1.Karagül T, Yurtay N, Öneç B. Diagnosing Hematological Disorders Using Deep Learning Method. SAUCIS. 2021;4:227–243.

MLA

Karagül, Tuba, et al. “Diagnosing Hematological Disorders Using Deep Learning Method”. Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 2, Aug. 2021, pp. 227-43, doi:10.35377/saucis.04.02.836375.

Vancouver

1.Tuba Karagül, Nilüfer Yurtay, Birgül Öneç. Diagnosing Hematological Disorders Using Deep Learning Method. SAUCIS. 2021 Aug. 1;4(2):227-43. doi:10.35377/saucis.04.02.836375

Öz

Diagnosing Hematological Disorders Using Deep Learning Method

Abstract

Keywords

Supporting Institution

Project Number

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite