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%.

multiple sclerosis, voluntary disclosure, machine learning, multiple sclerosis diagnosis

Received: April 7, 2024; Revised: April 28, 2024; Accepted: May 3, 2024; Published: May 14, 2024

How to cite this article: Rawan A. Al-Mutairi and Amal Aljohani, A prediction model investigating voluntary sharing of information by people living with multiple sclerosis, JP Journal of Biostatistics 24(2) (2024), 287-307. https://doi.org/10.17654/0973514324018

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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