Prediction of Systemic Lupus Erythematosus using Machine Learning applied to Hair Fluorescence Spectroscopy Data

Sarra Ben Brik; Imen Cherni; Mehdi Somai; Hassen Ghalila; Sami Hamzaoui

doi:10.2991/978-94-6463-654-3_3

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Prediction of Systemic Lupus Erythematosus using Machine Learning applied to Hair Fluorescence Spectroscopy Data

Authors

Sarra Ben Brik¹^{, *}, Imen Cherni¹^{, 3}, Mehdi Somai², Hassen Ghalila¹^{, 4}, Sami Hamzaoui⁵

¹Department of Physics, Laboratoire de Spectroscopie Atomique et Moléculaire & Applications (LSAMA), Faculty of Science of Tunis, Tunis El Manar University, Tunis, P.O. Box 2092, Tunis, Tunisia

²Internal Medicine Department, Habib Thameur Hospital, Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia

³Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis, Tunisia

⁴Léonard de Vinci Pôle Universitaire, Research Center, 92916, Paris La Défense, France

⁵Department of Radiologic Technology, College of Applied Medical Sciences, Qassim University Burdayah, Buraydah, 51452, P.O. Box 6666, Saudi Arabia

^*Corresponding author. Email: sarra.benbrik@etudiant-fst.utm.tn

Corresponding Author

Sarra Ben Brik

Available Online 24 February 2025.

DOI: 10.2991/978-94-6463-654-3_3 How to use a DOI?
Keywords: Systemic Lupus Erythematosus; Front Face Fluorescence Spectroscopy; hair; machine learning; Multi-Layer Perceptron; Adaptive Boosting; Random Forest; XGboost
Abstract: Systemic Lupus Erythematosus (SLE) is an autoimmune disease which can affect multiple organs in the human body. Many reasons are remaining as a factor for this disease such as environmental, hormonal and genetic factors. SLE diagnosis is complicated and is done following well-established classification criteria and analyses, which can be invasive, costly, and time-consuming. In this paper we apply four Machine Learning (ML) Methods, Multi-Layer Perceptron (MLP), Adaptive Boosting (AdaBoost), Random Forest (RF) and XGboost on measures obtained using Front Face Fluorescence Spectroscopy applied on hair of a cohort composed of two series of data Healthy Controls (HC) and Lupus patients obtained from Habib Thameur Hospital. The measures taken for Lupus patients were classified into 3 groups according to the stages of disease evolution, the flare group, the R6M-3Y group which refers to patients in remission for a period between 6 months and 3 years, and R>3Y group which refers to patients in remission for over 3 years.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
Series: Atlantis Highlights in Intelligent Systems
Publication Date: 24 February 2025
ISBN: 978-94-6463-654-3
ISSN: 2589-4919
DOI: 10.2991/978-94-6463-654-3_3 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Sarra Ben Brik
AU  - Imen Cherni
AU  - Mehdi Somai
AU  - Hassen Ghalila
AU  - Sami Hamzaoui
PY  - 2025
DA  - 2025/02/24
TI  - Prediction of Systemic Lupus Erythematosus using Machine Learning applied to Hair Fluorescence Spectroscopy Data
BT  - Proceedings of the  International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
PB  - Atlantis Press
SP  - 23
EP  - 32
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-654-3_3
DO  - 10.2991/978-94-6463-654-3_3
ID  - Brik2025
ER  -

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