Variational Encoder Based Synthetic Alzheimer's Data Generation for Deep Learning, XGBoost and Statistical Survival Analysis

Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencoders (VAEs), to generate synthetic datasets that replicate real-world AD data while preserving privacy. These datasets were used to train and compare survival analysis models, including Survival XGBoost, Survival Transformers, and the Cox Proportional Hazards model. Results show that deep learning and boosting models outperform the standard Cox model in predicting the progression of AD, achieving a higher degree of accuracy. Our findings suggest that combining synthetic data with advanced machine learning models can improve predictive capabilities in healthcare research, particularly when real patient data is restricted.