Machine Learning for Genetic Marker Identification in Rare Neurological Disorders: A Comprehensive Review for Early Diagnosis

Abstract

Millions of people worldwide suffer from rare neurological conditions, which can be difficult to diagnose because of their varied clinical presentations and our incomplete knowledge of the underlying genetic pathways. The identification of genetic markers linked to these illnesses has been made possible by machine learning (ML) techniques, which may lead to an earlier and more precise diagnosis. This comprehensive review examines current ML methodologies applied to genetic marker identification in rare neurological disorders, evaluating their effectiveness, limitations, and clinical implications. We analyze various ML algorithms, including deep learning, ensemble methods, and feature selection techniques, discussing their applications in genomic data analysis, variant classification, and phenotype-genotype correlation. Along with emphasizing current developments and potential future paths in the discipline, the review also tackles issues including data scarcity, class imbalance, and interpretability.