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A Medical Information Recommender System For Disease Prediction And Its Treatment

1834 words - 8 pages

Divyang Srivastava
Department of Information
National Institute of
Technology Karnataka
Mani Shankar
Department of Information
National Institute of
Technology Karnataka
Mayank Pahadia
Department of Information
National Institute of
Technology Karnataka
The use of computer systems in decision making, prediction
and recommendation has been a trending topic
of research for more than a decade. The recent advances
in medical science can be attributed to advances
in computer technology. But, the prediction of medical
behavior is still a very challenging task which is
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VINCI 2014, August 5–8, 2014, Sydney, Australia.
Copyright 2014 ACM 978-1-4503-2765-7...$10.00
done with the help of a medical professional. The occurrence
of every disease shows a pattern based on its
symptoms. The main focus of this paper is to propose
a system to exploit these patterns for predicting the associated
diseases and the time that might be spent on
their treatment.
By prediction we mean to forecast an occurrence of a
condition based on some mathematical calculation. For
implementing this prediction, we need a recommender
system. A recommender system is a system which reads
an input, nds a pattern in it which is based on the
dataset given to train the system. Based on the pattern
it gures out a solution for the problem.
A nave solution can be to create a database of every
possible disease and its symptoms and predicting diseases
based on that. The biggest drawback about this
solution is that the eciency and speed of this solution
are very less and the size of this dataset would be very
The solution that we suggest in this paper is, of using
the symptoms with the ratings given by the patient,
to predict the possible diseases and the possible cure
time of these diseases. Our solution is novel and better
because we predict the diseases based on the severity of
the patients symptoms and the cure time prediction is
based on real-life data given by other patients. For an
accurate prediction we have given dierent coecients
to all the symptoms possible for a disease.
The rest of the paper is organized as follows. In Section
2, we discuss about the related research done in this
eld. In Section 3, we give a thorough methodology
of our system. The experimental setup and the results
obtained by us are explained in Section 4. Finally the
conclusions and future work are given in Section 5.
S Sudha and S...

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