Machine learning in early diagnosis of neurological diseases: Advancing accuracy and overcoming challenges

Abstract

Neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and epilepsy pose significant challenges to public health owing to their complex pathophysiology. Early detection and accurate diagnosis are critical for effective intervention; however, traditional diagnostic methods often fall short in terms of sensitivity and specificity. Machine learning (ML) has shown great promise for overcoming these limitations by analyzing large-scale datasets, including neuroimaging and genomic data, to enhance diagnostic and predictive accuracy. This review explored the applications of ML in the prediction and diagnosis of Alzheimer’s disease, Parkinson’s disease, and epilepsy. We reviewed the principles of the ML algorithms, their performance in multimodal data analysis, and their potential for streamlining diagnostic workflows. Additionally, we discuss key challenges, such as model interpretability, data integration, and clinical adoption of ML technologies. Our goal was to highlight how ML can transform the prediction, diagnosis, and management of neurological disorders and ultimately improve patient outcomes.