Expert System for Detecting Ear Diseases Using Case Based Reasoning (CBR) Method

Abstract

The presence of innovation in the health sector generally experiences rapid changes and
updates in various parts of human life. Data innovation is used to handle information in
various ways to produce quality data, which is used for welfare purposes and is key data
for direction. Earache is a disease of the inner or middle ear which generally causes
different side effects on the ear. This ear infection generally attacks adults, adolescents,
and children, causing high clinical costs. Therefore, we really need a tool or framework
that can look like an expert in diagnosing a disease. The master framework is not used to
replace expert capacity but is only used as an inseparable framework. An expert system
is a structure that can imitate the thinking of a PC master and can handle problems that
specialists generally solve. The procedure used in this master framework is the Case
Based Reasoning (CBR) strategy because this technique provides a comparability value
for a case by remembering similar events that have occurred when using the data or
information to overcome new or eventual problems. This issue can be resolved by
applying the newly used settings. The best number of similarities between the third case
shows that this new case practically has the same or equivalent side effects as the case
with a higher score, especially the third old case, with a specific of 0.56, where the
precision value of the similarity data for the current state is 4 %. This causes the data to
be close between old cases and new cases.