Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis

Merna Youssef; Samer M. Sharfo; Hani Attar; Mohanad A. Deif; Mohamed Hafez; Ahmad Solyman

doi:10.1109/EICEEAI60672.2023.10590595

Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis

Merna Youssef, Samer M. Sharfo, Hani Attar, Mohanad A. Deif, Mohamed Hafez, Ahmad Solyman

Electrical Power Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

16 Downloads (Pure)

Abstract

In the last few years, machine learning has increased in popularity across many disciplines. This paper aims to comprehensively analyze the CatBoost classification algorithm in the context of Sarcoidosis. Analysis was undertaken to evaluate the performance of the CatBoost classification algorithm in comparison to other classifiers. The CatBoost algorithm outperformed other classifiers exploited in this study to identify and differentiate Sarcoidosis. Previous scholarly works ignored missing data observations or filled them with mean values; on the other hand, this study has uncovered that the SIL-2R feature holds significant importance in predicting the occurrence of Sarcoidosis, which improved the selection of treatment and its efficacy. A comprehensive understanding of Sarcoidosis is essential to accurately differentiating symptoms associated with this illness from those associated with other conditions. It is strongly recommended that the CatBoost algorithm be used for sarcoidosis prediction.

Original language	English
Title of host publication	2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)
Publisher	IEEE
Number of pages	7
ISBN (Electronic)	9798350373363
ISBN (Print)	9798350373370
DOIs	https://doi.org/10.1109/EICEEAI60672.2023.10590595
Publication status	Published - 18 Jul 2024
Event	2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence - Zarqa, Jordan Duration: 27 Dec 2023 → 28 Dec 2023 https://eiceeai.zu.edu.jo/ (Link to conference website)

Publication series

Name	International Engineering Conference on Electrical, Energy, and Artificial Intelligence
Publisher	IEEE
ISSN (Print)	None

Conference

Conference	2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence
Abbreviated title	EICEEAI 2023
Country/Territory	Jordan
City	Zarqa
Period	27/12/23 → 28/12/23
Internet address	https://eiceeai.zu.edu.jo/ (Link to conference website)

Keywords

CatBoost
Sarcoidosis
Swarm Optimizer

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Health Informatics

Documents and Links

10.1109/EICEEAI60672.2023.10590595

Author accepted manuscriptAuthor accepted manuscript, 417 KBLicence: CC BY

Cite this

Youssef, M., Sharfo, S. M., Attar, H., Deif, M. A., Hafez, M., & Solyman, A. (2024). Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis. In 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (International Engineering Conference on Electrical, Energy, and Artificial Intelligence). IEEE. https://doi.org/10.1109/EICEEAI60672.2023.10590595

@inproceedings{8b43c4b327bf4b2d8db4501ea16cec39,

title = "Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis",

abstract = "In the last few years, machine learning has increased in popularity across many disciplines. This paper aims to comprehensively analyze the CatBoost classification algorithm in the context of Sarcoidosis. Analysis was undertaken to evaluate the performance of the CatBoost classification algorithm in comparison to other classifiers. The CatBoost algorithm outperformed other classifiers exploited in this study to identify and differentiate Sarcoidosis. Previous scholarly works ignored missing data observations or filled them with mean values; on the other hand, this study has uncovered that the SIL-2R feature holds significant importance in predicting the occurrence of Sarcoidosis, which improved the selection of treatment and its efficacy. A comprehensive understanding of Sarcoidosis is essential to accurately differentiating symptoms associated with this illness from those associated with other conditions. It is strongly recommended that the CatBoost algorithm be used for sarcoidosis prediction.",

keywords = "CatBoost, Sarcoidosis, Swarm Optimizer",

author = "Merna Youssef and Sharfo, {Samer M.} and Hani Attar and Deif, {Mohanad A.} and Mohamed Hafez and Ahmad Solyman",

year = "2024",

month = jul,

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doi = "10.1109/EICEEAI60672.2023.10590595",

language = "English",

isbn = "9798350373370",

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publisher = "IEEE",

booktitle = "2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)",

address = "United States",

note = "2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023 ; Conference date: 27-12-2023 Through 28-12-2023",

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Youssef, M, Sharfo, SM, Attar, H, Deif, MA, Hafez, M & Solyman, A 2024, Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis. in 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). International Engineering Conference on Electrical, Energy, and Artificial Intelligence, IEEE, 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, Zarqa, Jordan, 27/12/23. https://doi.org/10.1109/EICEEAI60672.2023.10590595

Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis. / Youssef, Merna; Sharfo, Samer M.; Attar, Hani et al.
2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). IEEE, 2024. (International Engineering Conference on Electrical, Energy, and Artificial Intelligence).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis

AU - Youssef, Merna

AU - Sharfo, Samer M.

AU - Attar, Hani

AU - Deif, Mohanad A.

AU - Hafez, Mohamed

AU - Solyman, Ahmad

PY - 2024/7/18

Y1 - 2024/7/18

N2 - In the last few years, machine learning has increased in popularity across many disciplines. This paper aims to comprehensively analyze the CatBoost classification algorithm in the context of Sarcoidosis. Analysis was undertaken to evaluate the performance of the CatBoost classification algorithm in comparison to other classifiers. The CatBoost algorithm outperformed other classifiers exploited in this study to identify and differentiate Sarcoidosis. Previous scholarly works ignored missing data observations or filled them with mean values; on the other hand, this study has uncovered that the SIL-2R feature holds significant importance in predicting the occurrence of Sarcoidosis, which improved the selection of treatment and its efficacy. A comprehensive understanding of Sarcoidosis is essential to accurately differentiating symptoms associated with this illness from those associated with other conditions. It is strongly recommended that the CatBoost algorithm be used for sarcoidosis prediction.

AB - In the last few years, machine learning has increased in popularity across many disciplines. This paper aims to comprehensively analyze the CatBoost classification algorithm in the context of Sarcoidosis. Analysis was undertaken to evaluate the performance of the CatBoost classification algorithm in comparison to other classifiers. The CatBoost algorithm outperformed other classifiers exploited in this study to identify and differentiate Sarcoidosis. Previous scholarly works ignored missing data observations or filled them with mean values; on the other hand, this study has uncovered that the SIL-2R feature holds significant importance in predicting the occurrence of Sarcoidosis, which improved the selection of treatment and its efficacy. A comprehensive understanding of Sarcoidosis is essential to accurately differentiating symptoms associated with this illness from those associated with other conditions. It is strongly recommended that the CatBoost algorithm be used for sarcoidosis prediction.

KW - CatBoost

KW - Sarcoidosis

KW - Swarm Optimizer

U2 - 10.1109/EICEEAI60672.2023.10590595

DO - 10.1109/EICEEAI60672.2023.10590595

M3 - Conference contribution

AN - SCOPUS:85199992813

SN - 9798350373370

T3 - International Engineering Conference on Electrical, Energy, and Artificial Intelligence

BT - 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)

PB - IEEE

T2 - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence

Y2 - 27 December 2023 through 28 December 2023

ER -

Youssef M, Sharfo SM, Attar H, Deif MA, Hafez M, Solyman A. Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis. In 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). IEEE. 2024. (International Engineering Conference on Electrical, Energy, and Artificial Intelligence). doi: 10.1109/EICEEAI60672.2023.10590595

Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Documents and Links

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