A Deep Learning Approach to Predict Cognitive Decline in Parkinson’s Disease Using Nomogram-Based Diagnostics

Abstract

Cognitive decline is a prevalent and debilitating non-motor symptom of Parkinson’s Disease (PD), significantly affecting the quality of life and disease management strategies. Early and accurate prediction of cognitive impairment is essential for timely intervention and treatment planning. This study proposes a novel deep learning framework that integrates clinical, demographic, and neuropsychological parameters with nomogram-based diagnostic modeling to predict the risk of cognitive decline in PD patients. Utilizing a large, longitudinal dataset of PD cases, we employed convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract temporal and spatial patterns from multimodal data sources. The model was trained and validated using cross-validation techniques and demonstrated high accuracy, sensitivity, and specificity compared to traditional machine learning models. A nomogram was constructed from the most influential features identified by the deep learning model, providing an interpretable and clinically applicable diagnostic tool. The results highlight the potential of combining deep learning with nomogram visualization to enhance predictive accuracy and facilitate personalized prognosis in Parkinson’s Disease. This approach paves the way for integrating AI-based tools into clinical practice for early cognitive decline detection in neurodegenerative disorders.