Web Based Expert System for Diagnosis of Cattle Disease

Ethiopia, having the majority of its population living in rural areas and agriculture being the backbone of its economy, is one of the African countries with bigger cattle population. Despite this fact, the economic contribution made by this enormous amount of cattle is not satisfactory. This is mainly due to loss of productivity from endemic and trans-boundary cattle diseases. Addressing cattle health related problems in remote rural areas with limited number of veterinarians, especially during outbreaks, is the other issue that seriously damages its potential economic contribution. To address this issue, an expert system (ES) with hybrid reasoning consisting of case based reasoning (CBR) and rule based reasoning (RBR) engine is proposed for the diagnosis of cattle diseases. Symptoms provided by a user are taken as query and solution is searched from a similar case in the case base. RBR gets involved when CBR fails to identify a single disease. In addition to that, problems solved by RBR engine are stored as experiences to be utilized as data source for the learning module of CBR. The prototype was evaluated through system performance testing and user acceptance testing with corresponding results of 91.67% and 82.34% respectively. Its learning module performance was found to be 100%. The evaluation reveals that the proposed approach has a promising result to be used for cattle diagnosis purpose.