An Intelligent Sy
An Intelligent System For Diabet Diagnosis Based on
Combined Intelligent Algorithm and Risk Factors in Patients
Mohammad
Fiuzy *,a,b , Javad Haddadnia a, b, Nasrin
Mollaniac, Maryam Hashemian b, Kazem Hassan
pourd
a. Department of Biomedical, Faculty of
Electrical and Computer, Hakim Sabzevari University, Sabzevar, Iran
b. Research Center for Advanced Medical
Technologies, Sabzevar University of Medical Sciences, Sabzevar, Iran
c. Departments of Biology, Faculty of Basic
Sciences, Hakim Sabzevari University, Sabzevar, Iran
d.Department. of Clinical Sciences, Sabzevar
University of Medical Sciences, Sabzevar, Iran
*Sabzevar,
KHorasan Razavi, Iran, Postal Code : 65418-13187
Abstract: Diabetes occurs when the body is unable
to produce or respond properly to insulin which is needed to regulate glucose.
Besides contributing to heart disease, diabetes also increases the risks of
developing kidney disease, blindness, nerve damage, and blood vessel damage.
Diabetes disease diagnosis via proper interpretation of the diabetes data is an
important (classification) problem. Diabet Diagnosis is a very problematic
issue in medical diagnosis. Nowadays, many relatively complex clinical trials
are carried out. Early diagnoses of diabetes dramatically reduce injuries and
damage caused by the infection in community. In this study, a method for proper
diagnosis based on the optimal features of the Risk Factors in patients is
introduced. By Using a combined artificial intelligence methods, including
search algorithms (BGA1) to explore or search and select the best
features, Data mining methods (FCM2) got to classify and categorize
data (patient characteristics led to the diagnosis of non-patient) Neural
Network (NN3) for modeling or detection and identification of
structural parameters of the disease, diabetic patient has been detected. Then,
for better Comparison and show the Performance of the Proposed System, Patients
tested based on Eight Factors of World Health Organization (WHO4) to
Diabet Diagnosis by the same Intelligent System. The proposed system by using a
combination of these methods was successful to achieve 94.031 % precision for diabetic
patient identification. Accurate detection by combination and interaction of
these methods based on the optimal appearance and Risk features, introduced by
the proposed algorithm that Compared with the common methods of detection and
diagnosis of patients with one side and artificial methods of the authorities
on the other hand, its kind and even more accurate than other methods, the
result is a smart combination. It's on operation kind has better than even more
intelligent system that had been introduced, given in this document.
[M. Fiuzy, J. Haddadnia, N. Mollania, M.
Hashemian, K. Hassan pour. An
Intelligent System For Diabet Diagnosis Based on Combined Intelligent Algorithm
and Risk Factors in Patients. Life
Sci J 2013;10(4s):380-386] (ISSN:1097-8135).http://www.lifesciencesite.com. 57
Keywords: Diabet, Risk Factor, Artificial Intelligent Process.
