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A PREDICTION MODEL INVESTIGATING VOLUNTARY SHARING OF INFORMATION BY PEOPLE LIVING WITH MULTIPLE SCLEROSIS
Keywords:
multiple sclerosis, voluntary disclosure, machine learning, multiple sclerosis diagnosisDOI:
https://doi.org/10.17654/0973514324018Abstract
Multiple Sclerosis (MS) is an autoimmune condition marked by the progressive degeneration of the central nervous system, particularly affecting the brain and spinal cord. This disease arises when the immune response inappropriately targets the myelin sheath of nerve fibers, disrupting neural communication and potentially leading to nerve damage. One of the critical challenges for patients with MS is the decision to disclose their diagnosis, a decision that carries considerable consequences in personal, social, and professional domains. Despite the importance of this issue, there is a current research gap in understanding the factors influencing this decision. This research aims to address this gap, intending to enhance the quality of life for people living with MS by developing a predictive model for voluntary diagnosis disclosure. This research focuses on developing a predictive model to investigate the voluntary disclosure of diagnosis by individuals living with MS. The dataset was obtained from a survey that we conducted among individuals diagnosed with MS within the Kingdom of Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF). The findings reveal that Logistic Regression and Random Forest models demonstrated superior performance, achieving an accuracy of 98%.
Received: April 7, 2024
Revised: April 28, 2024
Accepted: May 3, 2024
References
P. D. Rumrill Jr, R. T. Roessler, J. Li, K. Daly and M. Leslie, The employment concerns of Americans with multiple sclerosis: perspectives from a national sample, Work 52(4) (2015), 735-748.
R. T. Roessler, P. D. Rumrill Jr, J. Li and M. J. Leslie, Predictors of differential employment statuses of adults with multiple sclerosis, Journal of Vocational Rehabilitation 42(2) (2015), 141-152.
L. Rzepinski, M. Zawadka-Kunikowska, J. Kucharczuk, J. Newton and P. Zalewski, New insights into the socio-economic aspects of multiple sclerosis in a cohort of Polish patients, Annals of Agricultural and Environmental Medicine, 28(1) (2021), 99-106.
E. McGahey, G. Waghorn, C. Lloyd, S. Morrissey and P. L. Williams, Formal plan for self-disclosure enhances supported employment outcomes among young people with severe mental illness, Early Intervention in Psychiatry 10(2) (2016), 178-185.
V. M. Kordovski, S. E. Frndak, C. S. Fisher, J. Rodgers, B. Weinstock-Guttman R. H. Benedict, Identifying employed multiple sclerosis patients at-risk for job loss: when do negative work events pose a threat? Multiple Sclerosis and Related Disorders 4(5) (2015), 409-413.
J. Maurino et al., Workplace difficulties, health-related quality of life, and perception of stigma from the perspective of patients with multiple sclerosis, Multiple Sclerosis and Related Disorders 41 (2020), 102046.
A. K. Kirk-Brown, P. A.Van Dijk, R. D. Simmons, M. P. Bourne and B. K. Cooper, Disclosure of diagnosis of multiple sclerosis in the workplace positively affects employment status and job tenure, Multiple Sclerosis Journal 20(7) (2014), 871-876.
S. E. Frndak, V. M. Kordovski, D. Cookfair, J. D. Rodgers, B. Weinstock-Guttman and R. H. Benedict, Disclosure of disease status among employed multiple sclerosis patients: association with negative work events and accommodations, Multiple Sclerosis Journal 21(2) (2015), 225-234.
P. Calabrese, G. Kobelt, J. Berg, D. Capsa and J. Eriksson, New insights into the burden and costs of multiple sclerosis in Europe: results for Switzerland, Multiple Sclerosis Journal 23(2_suppl) (2017), 192-203.
S. Abolhassani, A. Yazdannik, F. Taleghani and A. Zamani, Social aspects of multiple sclerosis for Iranian individuals, Disability and Rehabilitation 37(4) (2015), 319-326.
M. G. Jaworski III, T. A. Fuchs, M. G. Dwyer, C. Wojcik, R. Zivadinov, B. Weinstock-Guttman and R. H. Benedict, Conscientiousness and deterioration in employment status in multiple sclerosis over 3 years, Multiple Sclerosis Journal 27(7) (2021), 1125-1135.
L. Gill and S. M. Hynes, Disclosing a diagnosis in the workplace: perspective of people with multiple sclerosis, International Journal of Disability Management 15 (2020), e8.
C. Hategeka, A. L. Traboulsee, K. McMullen and L. D. Lynd, Association of unemployment and informal care with stigma in multiple sclerosis: evidence from the survey on living with neurological conditions in Canada, International Journal of MS Care 21(5) (2019), 214-225.
S. Kalantari, M. Karbakhsh, Z. Kamiab, Z. Kalantari and M. A. Sahraian, Perceived social stigma in patients with multiple sclerosis: a study from Iran, Acta Neurol. Taiwan 27(1) (2018), 1-8.
A. D. Bass et al., Effect of multiple sclerosis on daily activities, emotional well-being, and relationships: the global vsMS survey, International Journal of MS Care 22(4) (2020), 158-164.
R. Alshamrani, A. Althbiti, Y. Alshamrani, F. Alkomah and X. Ma, Model-driven decision making in multiple sclerosis research: existing works and latest trends, Patterns 1(8) (2020), 100121.
Z. EksI, M. Cakiroglu, C. Oz, A. AralaSmak, H. H. KaradeII and M. E. Ozcan, Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning, Arquivos de Neuro-psiquiatria 78 (2020), 789-796.
A. Ion-Margineanu, G. Kocevar, C. Stamile, D. M. Sima, F. Durand-Dubief, S. Van Huffel and D. Sappey-Marinier, Machine learning approach for classifying multiple sclerosis courses by combining clinical data with lesion loads and magnetic resonance metabolic features, Frontiers in Neuroscience 11 (2017), 257329.
Y. Karaca and S. Hayta, The significance of artificial neural networks algorithms classification in the multiple sclerosis and its subgroups, Significance 2(12) (2015), 1-7.
J. Yperman, T. Becker, D. Valkenborg, V. Popescu, N. Hellings, B. V. Wijmeersch and L. M. Peeters, Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis, BMC Neurology 20 (2020), 1-15.
S. O. Olatunji et al., Comprehensible machine-learning-based models for the pre-emptive diagnosis of multiple sclerosis using clinical data: a retrospective study in the eastern province of Saudi Arabia, International Journal of Environmental Research and Public Health 20(5) (2023), 4261.
C. Walton et al., Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS, Multiple Sclerosis Journal 26(14) (2020), 1816-1821.
M. AlJumah et al., Rising prevalence of multiple sclerosis in Saudi Arabia, a descriptive study, BMC Neurology 20 (2020), 1-7.
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