Prediction of Paediatric Systemic Lupus Erythematosus Patients Using Machine Learning

Authors

DOI:

https://doi.org/10.4108/eetpht.10.6386

Keywords:

Neural Network, Support Vector Machine, PLSE

Abstract

Paediatric systemic lupus erythematosus (pSLE) is an autoimmune disease where the body's immune system attacks its own tissues, leading to organ damage. Advances in medical technology and the integration of artificial intelligence have significantly reduced the mortality rate of pSLE patients and improved their quality of life. Various studies have explored the link between environmental pollution and pSLE, utilizing machine learning to identify common gene expressions associated with the disease. However, the application of machine learning, particularly neural networks, to predict the status of pSLE patients over different timeframes remains underexplored. This study aims to demonstrate the effectiveness of  support vector machines (SVMs) and neural networks in predicting the status of pSLE patients. Results show that without SMOTE balancing, both SVMs and neural networks achieved an accuracy of 68.09%, while neural networks achieved the highest accuracy of 77.78% after SMOTE balancing. Healthcare stakeholders can employ these machine learning techniques to provide early insights into patients' future health status based on their current condition, thereby improving patient outcomes.

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References

Angel Chamorro Quijano, S., Muñoz Melgarejo, M., Rodríguez, G., Marlene Muñoz Saenz, D., Caroline Muñoz Saenz, J. Analysis of the relationship of systemic lupus erythematosus with exogenous factors in Peru. 2021. 4th International Conference on Digital Medicine and Image Processing. 72-76. DOI: https://doi.org/10.1145/3506651.3506663

CDC. Systemic lupus erythematosus (SLE). Centers for Disease Control and Prevention. 2022. https://www.cdc.gov/lupus/facts/ detailed.html#:~:text=doing%20about%20SLE%3F,What%20is%20SLE%3F,%2C%20kidneys%2C%20and%20blood%20vessels.

Levy, D. M., Kamphuis, S. Systemic lupus erythematosus in children and adolescents. Pediatric Clinics of North America. 2021 59(2), 345–364. https://doi.org/10.1016/j.pcl.2012.03.007 DOI: https://doi.org/10.1016/j.pcl.2012.03.007

Singh, R. R.,Yen, E. Y. Sle mortality remains disproportionately high, despite improvements over the last decade. Lupus, 2018. 27(10), 1577–1581. DOI: https://doi.org/10.1177/0961203318786436

PSLEM. What is Sle? 2022. https://lupusmalaysia.org/en/what-is-sle

Descloux, E., Durieu, I., Cochat, P., Vital-Durand, D., Ninet, J., Fabien, N., Cimaz,R. Influence of age at disease onset in the outcome of paediatric systemic lupus erythematosus. Rheumatology. 2009. 48(7), 779–784. https://doi.org/10.1093/rheumatology/kep067 DOI: https://doi.org/10.1093/rheumatology/kep067

Chai, H. C., Phipps, M. E., Chua, K. H. Genetic risk factors of systemic lupus erythematosus in the Malaysian population: A Minireview. Clinical and Developmental Immunology. 2012, 1–9. https://doi.org/10.1155/2012/963730 DOI: https://doi.org/10.1155/2012/963730

Lee, H.-S., Bae, S.-C. What can we learn from genetic studies of systemic lupus erythematosus? implications of genetic heterogeneity among populations in SLE. Lupus. 2010. 19(12), 1452–1459. https://doi.org/10.1177/0961203310370350 DOI: https://doi.org/10.1177/0961203310370350

Lim, S. C., Chan, E. W.,Tang, S. P. Clinical features, disease activity and outcomes of Malaysian children with paediatric systemic lupus erythematosus: A cohort from a tertiary centre. Lupus. 2020. 29(9), 1106–1114. DOI: https://doi.org/10.1177/0961203320939185

Nazri, S. K., Wong, K. K., Hamid, W. Z. Pediatric systemic lupus erythematosus. Saudi Medical Journal. 2018. 39(6), 627–631. https://doi.org/10.15537/smj.2018.6.22112 DOI: https://doi.org/10.15537/smj.2018.6.22112

Blaskievicz, P. H., Silva, A. M., Fernandes, V., Junior, O. B., Shimoya-Bittencourt, W., Ferreira, S. M., da Silva, C. A. Atmospheric pollution exposure increases disease activity of systemic lupus erythematosus. International Journal of Environmental Research and Public Health. 2020. 17(6), 1984. DOI: https://doi.org/10.3390/ijerph17061984

Lim, S. C., Yusof, Y. L., Johari, B., Kadir, R. F., Tang, S. P. Neuropsychiatric lupus in Malaysian children: Clinical characteristics, imaging features and 12-month outcomes. The Turkish Journal of Pediatrics. 2021.63(5), 743. DOI: https://doi.org/10.24953/turkjped.2021.05.002

Rajimehr, R., Farsiu, S., Kouhsari, L. M., Bidari, A., Lucas, C., Yousefian, S., Bahrami, F. Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks. Lupus. 2002. 11(8), 485–492. DOI: https://doi.org/10.1191/0961203302lu226oa

Robinson, G. A., Peng, J., Dönnes, P., Coelewij, L., Naja, M., Radziszewska, A., Wincup, C., Peckham, H., Isenberg, D. A., Ioannou, Y., Pineda-Torra, I., Ciurtin, C., Jury, E. C. Disease-associated and patient-specific immune cell signatures in juvenile-onset systemic lupus erythematosus: Patient stratification using a machine-learning approach. The Lancet Rheumatology. 2020. 2(8). DOI: https://doi.org/10.1016/S2665-9913(20)30168-5

Baeldung. Multiclass classification using support Vector Machines. Baeldung on Computer Science. 2022. https://www.baeldung.com/cs/svm-multiclass-classification

goelaparna1520. Support Vector Machine in machine learning. GeeksforGeeks. 2023. https://www.geeksforgeeks.org/support-vector-machine-in-machine-learning/

Nicholson, C. A beginner's Guide to Neural Networks and deep learning. Pathmind. 2022. https://wiki.pathmind.com/neural- network

Singh, J. K. J., Ponnusamy, R. R., Ling, E. C. W., Chin, L. S. Early Prediction of Lupus Disease: A Study on the Variations of Decision Tree Models. Advances in Bioengineering and Biomedical Science Research. 2022. 5(4). DOI: https://doi.org/10.33140/ABBSR.05.04.02

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Published

24-06-2024

How to Cite

1.
Ponnusamy RR, Cheak LC, Ling ECW, Chin LS. Prediction of Paediatric Systemic Lupus Erythematosus Patients Using Machine Learning. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Jun. 24 [cited 2024 Jul. 13];10. Available from: https://publications.eai.eu/index.php/phat/article/view/6386