Objective(s): The study aims to assess the early detection of early detection of first degree relatives to type-II
diabetes mellitus throughout the diagnostic tests of Glycated Hemoglobin A1C. (HgbA1C), Oral Glucose Tolerance
Test (OGTT) and to find out the relationship between demographic data and early detection of first degree
relatives to type-II diabetes mellitus.
Methodology: A purposive "non-probability" sample of (200) subjects first degree relatives to type-II diabetes
mellitus was selected from National Center for Diabetes Mellitus/Al-Mustansria University and Specialist Center
for Diabetes Mellitus and Endocrine Diseases/Al-kindy. These related persons have presented the age of (40-70)
years old. A questionnaire was constructed for the purpose of the study, it is composed of (3) major parts, and
overall items, which are included in the questionnaire are (76) items. Reliability and validity of the questionnaire
were determined through a pilot study which is carried out during the period of August, 1
st
, 2008 to February, 30th
2009. The study instrument and structured interview technique were used as means of data collection. The data
were analyzed through the application of the descriptive statistical data analysis approach (Frequency and
Percentage) and the inferential statistical data analysis approach Chi-square, Pearson correlation coefficient.
Results: The results of the study confirmed that the mean of age is (55.7) year, and the majority of the sample are
male, first degree relatives with diabetes mellitus type-II are within positive bio-social aspect and laboratory
screening had an effect on the incidence of diabetes mellitus type-II for first degree relatives to type-II diabetes
mellitus.
Recommendations: The study recommends that the number of diabetes centers should be increased in Baghdad
and Governorates, promote of HbA1c test from general hospitals laboratories, guide notebook about the
predisposing factors of diabetes mellitus in his family, periodic screening for pre-diabetes and diabetes in high risk,
asymptomatic, undiagnosed adults within the health care setting, prevention program to prevent and control on
the predisposing risk factors for nondependent diabetes mellitus type-II and complication
Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The
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