The potential of machine learning to personalized medicine in Neurogenetics: Current trends and future directions

Neurogenetic disorders (NeD) are a group of neurological conditions resulting from inherited genetic defects. By affecting the normal functioning of the nervous system, these diseases lead to serious problems in movement, cognition, and other body functions. In recent years, machine learning (ML) approaches have proven highly effective, enabling the analysis and processing of vast amounts of medical data. By analyzing genetic data, medical imaging, and other clinical data, these techniques can contribute to early diagnosis and more effective treatment of NeD. However, using these approaches is challenged by issues including data variability, model explainability, and the requirement for interdisciplinary collaboration. This paper investigates the impact of ML on healthcare diagnosis and care of common NeD, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and Multiple Sclerosis disease (MSD). The purpose of this research is to determine the opportunities and challenges of using these techniques in the field of neurogenetic medicine. Our findings show that using ML can increase the detection accuracy by 85 % and reduce the detection time by 60 %. Additionally, the use of these techniques in predicting patient prognosis has been 70 % more accurate than traditional methods. Ultimately, this research will enable medical professionals and researchers to leverage ML approaches in advancing the diagnostic and therapeutic processes of NeD by identifying the opportunities and challenges.