Objective: is to determine the level of awareness concerning the reproductive health among adolescent girls in
Baghdad City.
Methodology: A cross sectional study was performed in order to assess the level of awareness regarding reproductive
health among 180 adolescent school girls in the age 12-18 years from five secondary schools in Al-Seder Sector in
Baghdad city, the data was collected by direct interview using constructed questionnaire to obtain socio-demographic
characteristics and level of awareness related to reproductive health. The study started from September 2012 to
January 2013.
Results: the results show that the highest percentage of girls (47.7%) was in age (17-18) year's age, (54.5%) at 4th class
secondary school. The primary school is the educational background of their mothers (40.5%), (41.6%) of the
respondents who have large family size. Television and internet were the first source of information (40.9%) of
respondents. Good awareness regarding menstruation was recorded for (45.5%) of respondents, also good awareness
regarding pubertal body changes among (51.1%) of respondents. Poor awareness recorded among respondents
regarding both gynecological problems (45.6%) and (AIDS) (60.5%).
Recommendations: the present study recommends that priority should be given to provide health education to
increase the level of awareness regarding reproductive health for adolescent girls in Baghdad City especially that is
related to gynecological problems and AIDS.
This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe
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