Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using

In the present paper, chitosan Schiff base has been synthesized from chitosan’s reaction with the salicyldehyde. The AuNPs was manufacture by extract of onion peels as a reducing agent. The Au NPs that have been prepared were characterized through the UV-vis spectroscopy, XRD analyses and SEM microscopy. The polymer blends of the chitosan Schiff base / PVP has been prepared through using the approach of solution casting. Chitosan Schiff base / PVP Au nano-composites was prepared. Nano composites and polymer blends have been characterized by FTIR which confirm the formation of Schiff base by revealing a new band of absorption at 1651cm^-1 as a result of the (C=N) imine group. SEM, DSC and TGA confirms the thermal stability of

In the present paper, chitosan Schiff base has been synthesized from chitosan’s reaction with the salicyldehyde. The AuNPs was manufacture by extract of onion peels as a reducing agent. The Au NPs that have been prepared were characterized through the UV-vis spectroscopy, XRD analyses and SEM microscopy. The polymer blends of the chitosan Schiff base / PVP has been prepared through using the approach of solution casting. Chitosan Schiff base / PVP Au nano-composites was prepared. Nano composites and polymer blends have been characterized by FTIR which confirm the formation of Schiff base by revealing a new band of absorption at 1651cm-1 as a result of the (C=N) imine group. SEM, DSC and TGA confirms the thermal stability of the pr

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

Breast 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

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Background: Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality among women in Iraq forming 23% of cancer related deaths. The low survival from the disease is a direct consequence to the advanced stages at diagnoses. Aim: To document the composite stage of breast cancer among Iraqi patients at the time of diagnosis; correlating the observed findings with other clinical and pathological parameters at presentation. Patients and Methods: A retrospective study enrolling the clinical and pathological characteristics of 603 Iraqi female patients diagnosed with breast cancer. The composite stage of breast cancer was determined according to UICC TNM Classification System of Breast Cancer and the Ameri

Here, a high sensitive method for biomarker identification according to nanostructure, using enzyme-linked immunosorbent assays (ELISAs), called Nano-ELISA, was presented. Different shapes of gold nanostructures (star and sphere; GNSs and GNPs) with a particle size of 40 nm for sphere particles were altered with a monoclonal antibody (Ab) as a detector Ab. To amplify the optical signal, gold nanostructures were employed as carriers of the signaling specific antibody against insulin growth factor binding protein- 3 (IGFBP-3). The substrate was catalytically oxidized by the Horseradish Peroxidase (HRP) conjugated gold nanostructure, and HRP also enhanced the optical signals, reflecting the amount of the targeting IGFBP-3. In comparison to t

Breast cancer is one of the most widespread cancers,depending on World Health Organization, cancer calculated for approximately 7.6 million incidences in 2008, whoever expected elevation in incidence is about 13.1 million in 2030. So that the current research investigates vitamin D role in the occurrence of this disease and explains if vitamin D has a positive effect on the incidence of disease, as well as measuring parathyroid hormone and estrogen levels. Three groups were included in this analysis: control healthy women, benign and malignant breast tumor women. All cases that were selected at the beginning of the disease diagnosis. According to statistical values vitamin D showed highly significant (P<0.001) decrease in benign (3.74±2.33