Trustworthy Predictive Uncertainty Analysis Through Artificial Neural Network‐Based Decision‐Making

Uncertainty quantification is also an area of classification of Alzheimer's Disease (AD) that has been largely underexplored using multimodal deep learning. Although there have been significant successes in the detection of AD, the factor of predictive uncertainty has not been widely addressed, especially in relation to the diagnosis of AD. The precision of uncertainty estimation is vital for assessing model reliability, as acquiring multimodal data is inherently complex and diagnostic errors may result in very high clinical risks. The inclusion of uncertainty measures can make models more interpretable and indicate predictions that may require additional clinical evaluation. This paper proposes a new density‐based evidential network, the Kernel Density Estimation Evidential Deep Learning Network (KDE‐EDLNet), that captures aleatoric uncertainty and epistemic uncertainty. Multimodal inputs to the model were tested using the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) databases. The results of the experiment indicate that the given approach offers superior predictive performance than current techniques and underscore the importance of quantifying uncertainty to improve the reliability and clinical validity of model predictions.