Advanced Neural Network Models for Predictive Analytics and Healthcare Management in Neurodegenerative Diseases

Abstract

The proposed method, HybridNet-NDM, integrates three vital algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs)-in a synergistic manner for applications in predictive analytics and healthcare management of neurodegenerative diseases. CNNs play a role in extracting biomarkers from medical imaging data, identifying complex patterns crucial for pinpointing neurodegenerative diseases. Meanwhile, LSTMs take charge of modeling the temporal dimensions of patient data, utilizing longitudinal records to predict disease progression. GCNs contribute by analyzing brain connectivity patterns, shedding light on disease development through an examination of brain structure and function. This hybrid neural network marries the strengths of these algorithms, fusing their unique features to attain unparalleled predictive accuracy. An attention mechanism is incorporated to further hone the feature fusion process, ensuring precision in predictions. Demonstrating improved performance across a range of metrics, the optimized model stands out in its effectiveness for predictive analytics and healthcare management in the realm of neurodegenerative diseases.