A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Bihter Daş

doi:10.35377/saucis.03.03.815556

EN

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Abstract

In recent years, different approaches and methods have been proposed to diagnose various diseases accurately. Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend to be specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases. In this study, we compare the accuracy of different classification methods supported by the SAS software suite, such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), and HP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided by the University of California, Irvine (UCI) repository is used. Experimental results show that based on the metrics of our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree do the lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rate and HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers and practitioners working in related fields.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence , Software Engineering

Journal Section

Research Article

Authors

Bihter Daş ^*
0000-0002-2498-3297
Türkiye

Publication Date

December 30, 2020

Submission Date

October 24, 2020

Acceptance Date

December 18, 2020

Published in Issue

Year 2020 Volume: 3 Number: 3

DOI

https://doi.org/10.35377/saucis.03.03.815556

IZ

https://izlik.org/JA24WB98SY

Cite

RIS / Bibtex

APA

Daş, B. (2020). A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases. Sakarya University Journal of Computer and Information Sciences, 3(3), 366-375. https://doi.org/10.35377/saucis.03.03.815556

AMA

1.Daş B. A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases. SAUCIS. 2020;3(3):366-375. doi:10.35377/saucis.03.03.815556

Chicago

Daş, Bihter. 2020. “A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases”. Sakarya University Journal of Computer and Information Sciences 3 (3): 366-75. https://doi.org/10.35377/saucis.03.03.815556.

EndNote

Daş B (December 1, 2020) A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases. Sakarya University Journal of Computer and Information Sciences 3 3 366–375.

IEEE

[1]B. Daş, “A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases”, SAUCIS, vol. 3, no. 3, pp. 366–375, Dec. 2020, doi: 10.35377/saucis.03.03.815556.

ISNAD

Daş, Bihter. “A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases”. Sakarya University Journal of Computer and Information Sciences 3/3 (December 1, 2020): 366-375. https://doi.org/10.35377/saucis.03.03.815556.

JAMA

1.Daş B. A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases. SAUCIS. 2020;3:366–375.

MLA

Daş, Bihter. “A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases”. Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, Dec. 2020, pp. 366-75, doi:10.35377/saucis.03.03.815556.

Vancouver

1.Bihter Daş. A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases. SAUCIS. 2020 Dec. 1;3(3):366-75. doi:10.35377/saucis.03.03.815556