Experiments Using Belief Functions and Weights of Evidence incorporating Statistical Data and Expert Opinions

TL;DR

This paper explores the use of belief functions and weights of evidence for managing uncertainty in medical diagnosis, integrating statistical data with expert opinions, especially in data-scarce and complex scenarios.

Contribution

It introduces a methodology for applying belief functions to statistical medical data and demonstrates how expert opinions can be incorporated at multiple levels.

Findings

Belief functions effectively handle missing and diverse data types.

Expert modification improves belief accuracy.

Comparison shows weights of evidence can outperform logistic regression in certain cases.

Abstract

This paper presents some ideas and results of using uncertainty management methods in the presence of data in preference to other statistical and machine learning methods. A medical domain is used as a test-bed with data available from a large hospital database system which collects symptom and outcome information about patients. Data is often missing, of many variable types and sample sizes for particular outcomes is not large. Uncertainty management methods are useful for such domains and have the added advantage of allowing for expert modification of belief values originally obtained from data. Methodological considerations for using belief functions on statistical data are dealt with in some detail. Expert opinions are Incorporated at various levels of the project development and results are reported on an application to liver disease diagnosis. Recent results contrasting the use of weights of evidence and logistic regression on another medical domain are also presented.