Breast cancer was one of the most common reasons for death among the women in the world. Limited awareness of the seriousness of this disease, shortage number of specialists in hospitals and waiting the diagnostic for a long period time that might increase the probability of expansion the injury cases. Consequently, various machine learning techniques have been formulated to decrease the time taken of decision making for diagnoses the breast cancer and that might minimize the mortality rate. The proposed system consists of two phases. Firstly, data pre-processing (data cleaning, selection) of the data mining are used in the breast cancer dataset taken from the University of California, Irvine machine learning repository in this stage we modified the Correlation Feature Selection (CFS) with Best First Search (BFS) established on the Discriminant Index (DI) so as to reduce the complexity of time and get high accuracy. Secondly, Bayesian Rough Set (BRS) classifier is applied to predict the breast cancer and help the inexperienced doctors to make decisions without need the direct discussion with the specialist doctors. The result of experiments showed the proposed system give high accuracy with less time of predication the disease.
FLI1 is a member of ETS family of transcription factors that regulate a variety of normal biologic activities including cell proliferation, differentiation, and apoptosis. The expression of FLI1 and its correlation with well-known breast cancer prognostic markers (ER, PR and HER2) was determined in primary breast tumors as well as four breast cancer lines including: MCF-7, T47D, MDA-MB-231 and MDA-MB-468 using RT-qPCR with either 18S rRNA or ACTB (β-actin) for normalization of data. FLI1 mRNA level was decreased in the breast cancer cell lines under study compared to the normal breast tissue; however, Jurkat cells, which were used as a positive control, showed overexpression compared to the normal breast. Regarding primary breast carcinoma
... Show MoreThe current study was conducted on 100 females who were divided into two main groups; 60 with breast cancer and 40 healthy controls. Blood samples were collected from both premenopausal and postmenopausal breast cancer and healthy women. The samples were appropriately processed for the analysis of trace elements (zinc, copper, and lead) by using flame atomic absorption spectrophotometry (FAAS). The results showed a highly significant decrease (p< 0.01) in the mean serum level of zinc of in both pre- and postmenopausal breast cancer women (71.7 + 5.1 and 70.4 + 5.4 µg/dL, respectively) compared with healthy controls (89.7 + 10.2 and 97.5 + 13.2 µg/dL, respectively) . Also, a highly significant
... Show MoreWisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.
Breast cancer (BC) is the most common malignancy in women worldwide and a major cause of cancer-related deaths for women in Iraq. This assignment was created to investigate the characteristics of BC diagnosed in Baghdad from 2018 to 2021. A total of eighty-nine of paraffin embedded tissue blocks of different breast tissue tumors (71 females and 18 males) with their data, were collected from archive of Histopathology Department, Teaching Laboratories of Medical City, Al-Yarmouk Teaching Hospital, and a private laboratory in Baghdad-Iraq. The clinical information regarding age, gender, tumor size, tumor stage and grade, lymph nodes metastasis, in addition to the findings of estrogen receptor (ER), progesterone receptor (PR), human
... Show MoreHuman cytomegalovirus (HCMV) has a worldwide distribution and common infections. The presence of HCMV genome and antigens has been detected in many kinds of human cancers. The proto-oncogene Her2/neu is overexpressed in 25% to 30% of human breast cancers and frequently associated with tumor aggressiveness and worse prognosis. Examination was applied to detect different HCMV antigens and to demonstrate their correlation with Her2/neu overexpression in breast cancer. The present study includes samples from 70 women of 60 breast cancer patients and 10 normal breast tissues. Formalin-fixed paraffin embedded tissue blocks were obtained from each woman according to ethical approval. Human cytomegalovirus early antigen expression was detected in 5
... Show MoreHyperglycemia is a complication of diabetes (high blood sugar). This condition causes biochemical alterations in the cells of the body, which may lead to structural and functional problems throughout the body, including the eye. Diabetes retinopathy (DR) is a type of retinal degeneration induced by long-term diabetes that may lead to blindness. propose our deep learning method for the early detection of retinopathy using an efficient net B1 model and using the APTOS 2019 dataset. we used the Gaussian filter as one of the most significant image-processing algorithms. It recognizes edges in the dataset and reduces superfluous noise. We will enlarge the retina picture to 224×224 (the Efficient Net B1 standard) and utilize data aug
... Show MoreToxoplasmosis is regarded as one of the most important global life-threatening diseases in immune-compromised people. The intracellular protozoon Toxoplasma gondii is the causative pathogen of toxoplasmosis. Aim of this study is to investigate the possible association between T. gondii infection and breast cancer (BC) in Iraqi women, also to assess the effect of T. gondiion interleukin 10 (IL-10) of the immune response. By ELISA method, blood samples from 81 women with breast cancer and 60 apparently healthy women have been examined for presence of anti-toxoplasmaantibodies, also the levels of serum IL-10 were estimated in these subjects. Results showed that women with BC had the highest prevalence rate of toxoplasmosis. The anti- T.gondii
... Show MoreBreast cancer is the commonest cancer affecting women worldwide. Different studies have dealt with the etiological factors of that cancer aiming to find a way for early diagnosis and satisfactory therapy. The present study clarified the relationship between genetic polymorphisms of BRCA1 & BRCA2 genes and some etiological risk factors among breast cancer patients in Iraq. This investigation was carried out on 25 patients (all were females) who were diagnosed as breast cancer patients attended AL-Kadhemya Teaching Hospital in Baghdad and 10 apparently healthy women were used as a control, all women (patients and control) aged above 40 years. The Wizard Promega kit was used for DNA isolation from breast patients and normal individuals. B
... Show MoreBreast cancer (BC) is one of the most frequently observed malignancy in females worldwide. Today, tamoxifen (TAM) is considered as the highly effective therapy for treatment of breast tumors. Oxidative stress has implicated strongly in the pathophysiology of malignancies. This study aimed to investigate the changes in the levels of oxidants and antioxidants in patients with newly diagnosed and TAM-treated BC. Sixty newly diagnosed and 60 TAM-treated women with BC and 50 healthy volunteers were included in this study. Parameters including total oxidant capacity (TOC), total antioxidant capacity (TAC), and catalase (CAT) activity were determined before and after treatment with TAM. The serum levels of TOC and oxidative stress index (OSI) were
... Show MoreProjects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo