Technical Report XXVI
A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease
T Chatzimichail, AT Hatjimihail
Abstract
Medical diagnosis is central to patient care and healthcare delivery. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study aims to introduce a software tool for quantifying uncertainty in Bayesian diagnosis, which remains insufficiently explored. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and nondiseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.
First Published
2024-01-04
Revised
2026-08-11
DOI
Citation
Chatzimichail T, Hatjimihail AT. A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease. Technical Report XXVI. Hellenic Complex Systems Laboratory; 2024. https://doi.org/10.5281/zenodo.21892863.
Source Notebook (Revised on 2026-08-04)
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