HARNESSING ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND DIAGNOSIS OF NEUROLOGICAL DISORDERS

Abstract

Neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and epilepsy present significant global health burdens due to their progressive nature and the complexity of early detection. Traditional diagnostic techniques often fail to capture subtle early-stage biomarkers, resulting in delayed interventions and suboptimal patient outcomes. In recent years, artificial intelligence (AI) has emerged as a transformative force in healthcare, offering data-driven precision and speed in clinical decision-making.This study presents a comprehensive AI-driven diagnostic framework that integrates machine learning (ML), deep learning (DL), and natural language processing (NLP) to enhance early detection and personalized management of neurological disorders. The methodology involved the analysis of multimodal datasets including brain imaging, EEG recordings, and clinical text data using convolutional neural networks, support vector machines, and transformer-based NLP models. Feature-level and decision-level fusion techniques were employed to optimize diagnostic performance, while wearable sensor data supported real-time monitoring.The results demonstrate high diagnostic accuracy across all models, with CNNs achieving over 95% in early Alzheimer’s detection and NLP models effectively extracting linguistic markers indicative of Parkinson’s progression. Real-time analysis through wearable sensor integration enabled the detection of tremors and seizures with near-instantaneous latency. The ensemble model further improved specificity and interpretability using SHAP values and saliency mapping, confirming the reliability and clinical transparency of the predictions.In conclusion, this research validates the efficacy of AI in enhancing neurological diagnostics by offering scalable, precise, and explainable solutions. The integration of wearable technologies, multimodal analytics, and real-time feedback systems positions AI as a pivotal tool in proactive neurological care. Addressing challenges in ethics, data privacy, and model generalization will be essential to translating these findings into clinical practice.