PREDICTION OF ALZHEIMER'S DISEASE USING BAYESIAN NEURAL NETWORKS (Computer systems and information technologies, 2025)
This article presents a methodology for optimizing Bayesian neural networks and their application to complex prediction tasks, with a focus on diagnosing Alzheimer’s disease. Alzheimer’s is a neurodegenerative condition where early detection is vital for initiating timely interventions and improving patient outcomes. The proposed methodology includes determining the optimal structure of classical neural networks by performing grid search to identify the best combination of layers and neurons. The architecture identified through cross-validation forms the basis for constructing Bayesian neural networks, where weights derived from classical models are utilized as prior distributions. This integration improves prediction accuracy while preserving the Bayesian network’s capacity for quantifying uncertainty.Bayesian models are trained using Markov Chain Monte Carlo methods, with experiments exploring the impact of prior distribution parameters, including variations in means and standard deviations. Results show that a mean value of zero and a standard deviation of 2.5 yield optimal outcomes, minimizing classification error while balancing uncertainty estimation. Increasing the standard deviation improved performance up to a threshold, beyond which further gains were statistically insignificant. The ability of Bayesian neural networks to incorporate uncertainty provides critical advantages for decision-making in medical contexts, particularly in scenarios involving incomplete or noisy data.The findings demonstrate that Bayesian neural networks based on optimized classical architectures can effectively address prediction tasks in high-stakes domains like medicine. By leveraging prior knowledge, the proposed approach reduces training time and enhances model performance, offering a robust framework for diagnosing Alzheimer’s disease. Future research will explore automating structural optimization, assessing the impact of different prior distributions, and extending this methodology to other neurodegenerative disorders.
Keywords: Bayesian neural networks; Alzheimer's disease prediction; machine learning; a priori distributions; deep learning