Objective(s): To Evaluate Diabetes self –management among patients in Baghdad City and to compare
between these patients self-management relative to the type of the disease.
Methodology: A descriptive design was conducted in Baghdad city, started from November 16th 2017 to the
end of May 17 th 2018 in order to evaluate Diabetes self-management. Purposive (non-probability) sample,
which was consisted of (120) patients who were diagnosed with D.M. The sample is comprised of (60) patient
with diabetes type I and (60) patient with diabetes type II. It is consisted of (60) male and (60) female. A
questionnaire is constructed for the purpose of the study. It is composed of (42) items. Reliability and validity of
the questionnaire is determined through pilot study. Data are collected through the use of the questionnaire and
the structured interview as means of data collection. Data are analyzed through the application of descriptive
statistical data analysis approach of frequency, percentage, mean, range and total scores and inferential
statistical data analysis approach of t-test.
Results: The results show that more than a half have a poor level of diabetes self-management (n = 64; 53.3%)
and a lesser proportion who have a good level of such management.
Recommendations: Further study can be conducted on subjects with a large sample size. An education
program can be done to improve patient knowledge about the diabetes self-management. Better attention can be
directed for patients who were students, married and low income ones
Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.
In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.
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