Advancing the Prediction of Neurological Disorders Through Innovative Machine Learning Methodologies and Clinical Data Analysis

Abstract

Neurological disorders, such as Alzheimer's disease, Parkinson’s disease, and multiple sclerosis, pose significant diagnostic challenges due to their complex etiology and progressive nature. Early and accurate prediction of these conditions is critical for timely intervention and improved patient outcomes. This study presents a novel machine learning framework that integrates advanced algorithms including ensemble learning, deep neural networks, and temporal modeling with comprehensive clinical datasets comprising imaging, electronic health records (EHRs), laboratory results, and cognitive assessments. We evaluate the performance of several state-of-the-art models including Random Forest, XGBoost, BiLSTM, and 1D-CNN architectures, individually and in hybrid configurations, to enhance the prediction of disease onset and progression. The proposed framework achieves robust predictive accuracy and generalizability across multiple datasets, offering insights into key biomarkers and risk patterns. This work underscores the transformative potential of machine learning in precision neurology and contributes to the development of intelligent decision-support systems for clinical practice.