HCSL Software Repository

Bayesian Diagnosis

Chatzimichail T, Hatjimihail AT. Bayesian Diagnosis: A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis. Ver. 1.0.5. Drama: Hellenic Complex Systems Laboratory, 2023

Description

This software tool employs Bayesian inference to calculate the posterior probability of a disease diagnosis. It comprises three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions. The tool is designed to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations.

Datasets

The provided datasets, d1 (Fasting Plasma Glucose [mg/d]) in diabetics 40-60 years old), d2 (Glucated Hemoglobin A1c [%] in diabetics 40-60 years old), nd1 (Fasting Plasma Glucose [mg/dl] in nondiabetics 40-60 years old), and nd2 (Glucated Hemoglobin A1c [%] in nondiabetics 40-60 years old), were obtained from the database of the National Health and Nutrition Examination Survey (NHANES), Centers for Disease Control and Prevention, USA. They can be replaced by other datasets of two mesurands in diseased and nondiseased.

Source code (Revised on 09/11/2023)

Interface

Related publications

Software Requirements

Operating Systems: Microsoft Windows, Linux, Apple iOS

Programming language: Wolfram Language

Software source code file format: Wolfram Notebook

Other software requirements: Wolfram Player (freely available) or Wolfram Mathematica

Recommended system: Intel Core i9 or equivalent CPU and 32GB+ of RAM

Software license: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License