Objectives: to compare health of mothers and neonatal among age groups, to find out the correlation between
age groups and mother and neonatal health.
Methodology: A descriptive study was carried out at delivery rooms of three teaching hospitals in Baghdad city
from Feb. 28th through May. 28th
, 2013. A purposive (non-probability) sample of 300 laboring women was selected
from delivery rooms categorized into three groups, group 1 (≤19) years, group 2 their age between (20-35) years
old and group 3 their age (≥35) years. The data were collected through the use of constructing questionnaire, an
interview technique with mothers and reviewing their medical records as means of data collection; The
questionnaire consist of 3 parts: (1) demographic data (2) health of mothers during pregnancy (pregnancy
complications) (3) neonate health status. The validity of the study questionnaire was determined through a panel
of experts and the reliability of the study questionnaire was determine by the application of pilot study.
Descriptive statistical analysis procedure (frequencies and percentages) and inferential data analysis procedure
(chi-square, P-value, split- half approach and Cronbach's Alpha correlation coefficients) were applied for data
analysis.
Results: the finding of the study indicated that the highest percentages of all the study groups were primary school
graduates, urban and housewives. More than half of mothers in group 1 (teenage mothers) and group 2 (20-35)
years were related to their husband, while the highest percentages of mothers in group 3 (advanced maternal age)
were not related to their husband. The highest percentages of complications present in group 1 (teenage mothers)
were anemia, urinary tract infection, multiple pregnancy, Oligohydramnios, poly-hydramnios, intra uterine growth
retardation, Post-term labor, low Apgar score, low birth weight, congenital anomalies, respiratory distrees
syndrome, meconium aspiration syndrome and neonatal death. The highest percentages of complications present
in group 3 (advanced maternal age) were pregnancy induced hypertension, gestational diabetes, antepartum
haemorrhage, pre-term labor, premature rupture of membrane and macrosomia. There is a statistical significant
correlation between age groups and hypertension and preterm, Apgar score, neonatal weight and respiratory
distress syndrome.
Recommendations: Care providers need to be aware about the increasing of obstetrical and neonatal health
complications in the extremities of maternal age (teenage and advanced maternal age) and adjust obstetrical and
neonatal management protocols to ensure optimal maternal and neonatal health
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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