Avian Influenza (H5N1) Expert System using Dempster-Shafer Theory

TL;DR

This paper presents an expert system for identifying avian influenza (H5N1) in chickens using Dempster-Shafer theory to handle uncertainty, with successful identification demonstrated in a poultry-rich region of Indonesia.

Contribution

The study introduces a novel expert system employing Dempster-Shafer theory for avian influenza detection, specifically tailored for poultry in Lampung Province, Indonesia.

Findings

Successfully identified avian influenza using the expert system.

Demonstrated effective handling of uncertainty in disease identification.

Applied the system to a region with high poultry population.

Abstract

Based on Cumulative Number of Confirmed Human Cases of Avian Influenza (H5N1) Reported to World Health Organization (WHO) in the 2011 from 15 countries, Indonesia has the largest number death because Avian Influenza which 146 deaths. In this research, the researcher built an Avian Influenza (H5N1) Expert System for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Research location is in the Lampung Province, South Sumatera. The researcher reason to choose Lampung Province in South Sumatera on the basis that has a high poultry population. Dempster-Shafer theory to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Avian Influenza (H5N1) Expert System has successfully identified the existence of avian influenza and displaying the result of identification process.