Abstract:

Background: Multiple Sclerosis (MS) is a chronic autoimmune neurodegenerative disorder marked by a highly heterogeneous clinical course, making disease progression difficult to predict. Early and accurate prognostication is vital for optimizing treatment strategies and improving patient outcomes.

Objective: This study explores the application of big data analytics and machine learning techniques to enhance predictive modeling of MS progression by integrating multi-modal, publicly accessible datasets.

Methods: We leveraged neuroimaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), genetic data from the International

Multiple Sclerosis Genetics Consortium (IMSGC), and clinical data from the Multiple Sclerosis Data Sharing Platform (MSDSP) and UK Biobank. After

rigorous data preprocessing and integration, we employed both linear models (LASSO regression) and non-linear models (deep learning neural

networks) to identify novel biomarkers and complex patterns associated with MS progression. Model performance was evaluated using metrics such as R² for regression tasks, and interpretability was enhanced using SHapley Additive exPlanations (SHAP) values.

Results: The deep learning model achieved an R² of 0.78 in predicting the Expanded Disability Status Scale (EDSS) scores over a five-year period, outperforming traditional linear models. Key predictors included increased lesion volume in periventricular and infratentorial regions, cortical thinning in the temporal and parietal lobes, presence of the HLA-DRB1*15:01 allele, and novel single nucleotide polymorphisms (SNPs) in neural repair genes. Visualization techniques, including heat maps, progression charts, and network graphs, elucidated the intricate relationships among neuroimaging findings, genetic factors, and clinical variables influencing disease progression.

Conclusions: The integration of big data analytics and machine learning significantly enhances the predictive modeling of MS progression. Identifying novel biomarkers and understanding their interplay offers promising avenues for early diagnosis and personalized treatment strategies. Addressing challenges related to data integration, model interpretability, and ethical considerations is essential for translating these advancements into clinical practice

Keywords:

MULTIPLE SCLEROSIS, BIG DATA ANALYTICS, PREDICTIVE MODELING, MACHINE LEARNING, <br /> NEUROIMAGING, GENOMICS, DEEP LEARNING, BIOMARKERS, DISEASE PROGRESSION, PERSONALIZED MEDICINE