Ensemble model for Early Prediction of Neurological Disorders using Machine Learning

Abstract

Neurological disorders pose a significant challenge to global health, often leading to long-term disability and decreased quality of life. This study explores the potential of machine learning techniques in the early prediction of such disorders, aiming to improve patient outcomes through timely intervention. We analysed a diverse dataset comprising clinical records, neuroimaging data, and genetic markers from 5,000 patients across multiple healthcare centers. Our approach involved the development and comparison of several machine learning models, including random forests, support vector machines, and deep neural networks. The results demonstrate that our ensemble model, which combines these techniques, achieves a sensitivity of 87% and specificity of 92% in predicting the onset of neurological disorders up to 18 months before clinical diagnosis. Notably, the model showed particular strength in identifying early markers for Alzheimer's disease and Parkinson's disease. Feature importance analysis revealed that certain neuroimaging patterns and genetic variants played crucial roles in prediction accuracy. While promising, our findings also highlight the challenges in translating these predictive models into clinical practice, including issues of interpretability and the need for prospective validation. This research contributes to the growing body of evidence supporting the use of artificial intelligence in neurology and sets the stage for future studies on personalized risk assessment and targeted preventive strategies for neurological disorders.