Objective(s): To assess women’s knowledge about health promotion after a cesarean delivery and to determine the association between women’s knowledge and their demographic data of age, level of education, and monthly income.
Methodology: A descriptive design is carried out to assess women’s knowledge about health promotion after cesarean delivery at Maternity and Pediatric Hospital in Al-Samawa City. This study starts from 26th of September 2020 up to 16th March 2021. Sample of (100) woman who are at reproductive age, pregnant (prime or multipara) who have planned to have birth by elective cesarean section or had previous elective caesarian section without medical indication or women who had cesarean section with medical indicat

Objective: The study aimed to screen the prepubertal children for idiopathic scoliosis at earlier stages, and find
out the relationship between idiopathic scoliosis and demographic data such as age, sex, body mass index,
heavy backpacks, and heart & lung diseases.
Methodology: A descriptive study was conducted on screening program for prepubertal children in primary
schools at Baghdad city, starting from 24th of February to the end of October 2010. Non- probability
(purposive) sample of 510 prepubertal children were chosen from primary schools of both sides of Al-Karkh
and Al-Russafa sectors. Data was collected through a specially constructed questionnaire format include (24)
items multiple choice questions, and

Introduction: Infection control or hospital-acquired infections are the major concern of the health care system and agencies. Critical care nurses are on the first-line contact with the patients, so on, they are most vulnerable to acquired infections. It is really important to regularly check their knowledge and practices concerning infection control. Objectives: The study aims to identify the impact of years’ experience on nurses’ knowledge and practices concerning infection control in three hospitals and center (Baghdad teaching hospital, Ibn Al-Nafees hospital, and Ibn al-Bitar center) Methodology: Cross-sectional study was conducted, the study starting from 4th of July 2020 to 13th of November 2020. Non-probability (purposive) sampl

n this paper, we formulate three mathematical models using spline functions, such as linear, quadratic and cubic functions to approximate the mathematical model for incoming water to some dams. We will implement this model on dams of both rivers; dams on the Tigris are Mosul and Amara while dams on the Euphrates are Hadetha and Al-Hindya.

Objective(s): To evaluate and compare between Health Promotion Program for the Prevention of Epidemics at Primary Health Care Centers in Baghdad City.

Methodology: A descriptive study, using the evaluation and comparative approaches, is conducted to evaluate health promotion program for the prevention of epidemics at primary health care centers in baghdad city from October 15^th  2019 through March 1^st 2020. A purposive, non-probability, sample of (42) health promotion unit officers were recruited from the same number of primary health care centers which were divided into (14) main, (14) sub and (14) family medicine primary health care centers i

Monthly water samples from three stations in Diwanya river at Diwanyia city were collected during December 1999 to June 2000. Variables from each stations were determined including ; temperature, pH ,dissolved oxygen, dissolved carbon dioxide , alkalinity ,total hardness, calcium ,magnesium , phosphate, nitrite, nitrate, chlorophyll-a , and total number of phytoplankton .The river considered as fresh water , alkaline ,very hard .The parameters recorded at different values from up and down stream.

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att