A Review of Machine Learning Technologies in Multiple Sclerosis Disease Detection

Abstract

Abstract: Multiple sclerosis (MS) is a complex neurological disorder affecting the central nervous system, presenting challenges in accurate and timely diagnosis. This review paper provides a comprehensive overview of the evolving landscape of machine learning (ML) technologies employed in the detection and diagnosis of multiple sclerosis. Spanning the period from 2011 to 2022, the review synthesizes findings from a myriad of studies utilizing diverse data sources, including magnetic resonance imaging (MRI) and clinical data. The paper discusses various ML models and techniques applied in MS detection, with a focus on their strengths, limitations, and comparative performances. Key areas of exploration include the utilization of convolutional neural networks (CNN), support vector machines (SVM), and random forests (RF), among other approaches. The integration of advanced ML architectures, data augmentation strategies, and the incorporation of diverse data modalities are examined for their impact on diagnostic accuracy. Challenges such as distinguishing MS from diseases with similar symptoms, ensuring patient data confidentiality, and developing interpretable ML models are explored, along with proposed solutions. The review identifies promising opportunities for future research, including the implementation of secure platforms, the development of improved disease prognosis systems, and the combination of multiple data types for enhanced predictive capabilities. Additionally, the review emphasizes the potential for larger, multi-center datasets to bolster model reliability and advocates for the commercialization of ML solutions in MS diagnosis. In conclusion, this review consolidates current knowledge, highlights trends in ML applications for MS detection, and offers valuable insights to guide further advancements in this critical domain.