Background: Breast cancer is the most common malignancy affecting the Iraqi population and the leading cause of cancer related mortality among Iraqi women. It has been well documented that prognosis of patients depends largely upon the hormone receptor contents and HER-2 over expression of their neoplasm. Recent studies suggest that Triple Positive (TP) tumors, bearing the three markers, tend to exhibit a relatively favorable clinical behavior in which overtreatment is not recommended. Aim: To document the different frequencies of ER/PR/HER2 breast cancer molecular subtypes focusing on the Triple Positive pattern; correlating those with the corresponding clinico-pathological characteristics among a sample of Iraqi patients diagnosed with th

This cross-sectional, questionnaire-based study evaluated the knowledge, attitude and practice towards breast cancer and breast self-examination [‎BSE]‎ among 387 [‎302 females and 85 males]‎ educated Iraqis affiliated to 2 Iraqi universities. The participants were categorized into 3 occupations: student [‎71.3%]‎, teaching staff [‎10.3%]‎ and administrative staff [‎18.3%]‎. About half of the participants had a low knowledge score [‎< 50%]‎; only 14.3% were graded as [‎Good]‎ and above. Almost 75% of the participants believed that the best way to control breast cancer was through early detection and other possible preventive measures. Most participants [‎90.9%]‎ had heard of BSE, the main source of informatio

Breast cancer is the most prevalent malignancy among women worldwide, in Iraq it ranks the first among the population and the leading cause of cancer related female mortality. This study is designed to investigate the correlations between serum and tissue markers in order to clarify their role in progression or regression breast cancer. Tumor Markers are groups of substances, mainly proteins, produced from cancer cell or from other cells in the body in response to tumor.  The study was carried out from April 2018 to April 2019 with total number of 60 breast cancer women. The blood samples were collected from breast cancer women in postoperative and pretherapeutic who attended teaching oncology hospital of the medical city in Baghdad and

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

Bootstrap is one of an important re-sampling technique which has given the attention of  researches recently. The presence of outliers in the original data set may cause serious problem to the classical bootstrap when the percentage of outliers are higher than the original one. Many methods are proposed to overcome this problem such  Dynamic Robust Bootstrap for LTS (DRBLTS) and Weighted Bootstrap with Probability (WBP). This paper try to show the accuracy of parameters estimation by comparison the results of both methods. The bias , MSE and RMSE are considered. The criterion of the accuracy is based on the RMSE value since the method that provide us RMSE value smaller than other is con

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

Background: Breast cancer still a major cause of disability and mortality among women throughout the world. Lack of awareness and early detection programs in developing countries is a main reason for escalating the mortality.

Objectives: to assess level of awareness about breast cancer among university female students in Baghdad focusing on knowledge of breast cancer risk factors, warning symptoms and signs and knowledge about the screening method specially breast self-examination.

Methods: A cross-sectional study conducted over two months from first of march through April 2015 and included (240) female students in non- medical colleges at Al-Rusafa and A

The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode