Abstract:
Medical diagnosis is an important task that should
be performed as accurately and efficiently as possible since a
proper diagnosis may lead to increase the quality of life of
the people. Educating patients about the symptoms will
improve the chances of obtaining the right diagnosis and
obtaining prompt and correct treatment. In many automated
diagnosis systems, the results are influenced always by the
bias of the researchers' initial assumptions. What is needed
instead is an approach that minimizes human bias and
considers all relevant data in determining a diagnosis. This
paper reveals the design and implementation of IMedicare, a
system that discover the most suitable and accurate diagnosis
by extracting information on diseases. A proper clustering
mechanism is being used to categorize diseases into groups
according to the symptoms revealed. This system is proposed
to accommodate several features such as predicting the most
probable diseases through the symptoms, providing better
communication among the doctors and the patients
concealing the privacy and etc. Key areas of diagnosis are
identified during the development process of the system in
order to provide a better experience to the users through this
approach. Analysis were done after consulting several
medical doctors and based on their feedbacks. The proposed
system was implemented using a dataset extracted from
several doctors and the details from the web. Then the data
set was segmented using a clustering approach and the
system was evaluated using extensive datasets.
