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Diagnosis the Breast Cancer using Bayesian Rough Set Classifier

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.

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Publication Date
Thu Jan 13 2022
Journal Name
Iraqi Journal Of Science
Association between MethylenetetrahydrofolateReductase (MTHFR) GenePolymorphisms and breast cancer in sample of Iraqi women

Folate metabolism is fundamental and essential for DNA structure synthesis and repair. Change in genes that participate in folate metabolism can be linked with different types of malignant tumor, Therefore, this study was conducted to find out the association between methylenetetrahydrofolatereductaseMTHFR gene polymorphisms and risk of breast cancer in a sample of Iraqi patients. One Single Nucleotide Polymorphism ( SNP) including MTHFR C677T was calculated using a tetra primer ARMS PCR experiment assay. The results explained that (MTHFR C677T) consists of three genotype (CC, CT, TT), The CC genotype was the most frequent in patients and control group ( 40.00%) and(60.00%) ,respectively, while the lowest frequency was for TT genotype(26

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Publication Date
Mon Apr 02 2012
Journal Name
Eastern Mediterranean Health Journal
Knowledge, attitude and practice regarding breast cancer and breast self-examination among a sample of the educated population in Iraq

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

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Publication Date
Fri Oct 01 2021
Journal Name
Journal Of Al-rafidain University College For Sciences ( Print Issn: 1681-6870 ,online Issn: 2790-2293 )
The Use of Logistic Regression Model in Estimating the Probability of Being Affected By Breast Cancer Based On the Levels of Interleukins and Cancer Marker CA15-3

Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o

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Publication Date
Thu Jun 25 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Preparation and Characterization of Topical Letrozole Nanoemulsion for Breast Cancer

Letrozole (LZL) is a non-steroidal competitive aromatase enzyme system inhibitor. The aim of this study is to improve the permeation of LZL through the skin by preparing as nanoemulsion using various numbers of oils, surfactants and co-surfactant with deionized water. Based on solubility studies, mixtures of oleic acid oil and tween 80/ transcutol p as surfactant/co-surfactant (Smix) in different percentages were used to prepare nanoemulsions (NS). Therefore, 9 formulae of (o/w) LZL NS were formulated, then pseudo-ternary phase diagram was used as a useful tool to evaluate the NS domain at Smix ratios: 1:1, 2:1 and 3:1.

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Publication Date
Fri Dec 31 2021
Journal Name
Onkologia I Radioterapia
Scopus
Publication Date
Fri Apr 30 2021
Journal Name
Al-kindy College Medical Journal
The Expression of Epstein-Barr virus in Breast cancer in relation to age

Background: Breast cancer is the most common
malignancy affecting females worldwide. The association
of Epstein-Barr virus (EBV) with this cancer is a longstanding
interest to this field.
Aim: to investigate the presence of EBV in breast tumor
tissue in relation to age.
Patients and Methods: Paraffin-embedded tissue blocks
from 45 female patients with breast tumors (ranged in age
from 28 to 85 years) were retrieved. The cases were
grouped into two categories: group (A): included 30 cases
with breast carcinoma and group (B): included 15 cases
with benign breast diseases as a control group .The
expression of EBV protein was examined
immunohistochemically.
Results: Twelve (40%) of the 30 breast canc

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Publication Date
Mon Jun 01 2020
Journal Name
P J M H S
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Scopus
Publication Date
Mon Apr 01 2013
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Molecular Study of Human Mammary Tumor Virus in Iraqi Women with Breast Cancer

Background: Earlier reports related the presence of Mouse Mammary Tumor Virus -like gene sequences to human breast carcinoma. Mouse Mammary Tumor Virus -like gene is a retrovirus, namely, a virus containing reverse transcriptase which transcript its RNA to DNA in a process that enables genetic material from the retrovirus to become a part of the genes of an infected cell permanently. The virus that found in women was designated as Human Mammary Tumor Virus by the authors, who have investigated the presence of Human Mammary Tumor Virus sequences in a many human breast tissues and in many countries.

Objectives: Detect HMTV genome in Iraqi women of breast cancer.

Patients and Methods

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Publication Date
Mon Dec 25 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Knowledge, Attitudes and Barriers Towards Breast Cancer Health Education Among Iraqi Community Pharmacists

With the increasing prevalence of breast cancer among female internationally, occupies about 25% of all cases of cancer, with a measured 1.57 million up to date cases in 2012. Breast cancer has turn a most warning to health of female in Iraq, where it is the major cause of death among women after cardiovascular diseases, with a mortality rate of 23% related cancer. Recently there is a crucial requirement to include community pharmacists in health elevation activities to support awareness and early diagnosis of cancer, specially breast cancer. The aim of this study is to assess knowledge, attitude and perceived barriers amongst Iraqi community pharmacists towards health promotion of breast cancer. This study is cross sectional research. A

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Publication Date
Tue May 30 2023
Journal Name
Iraqi Journal Of Science
Entropy-Based Feature Selection using Extra Tree Classifier for IoT Security

      The Internet of Things (IoT) is a network of devices used for interconnection and data transfer. There is a dramatic increase in IoT attacks due to the lack of security mechanisms. The security mechanisms can be enhanced through the analysis and classification of these attacks. The multi-class classification of IoT botnet attacks (IBA) applied here uses a high-dimensional data set. The high-dimensional data set is a challenge in the classification process due to the requirements of a high number of computational resources. Dimensionality reduction (DR) discards irrelevant information while retaining the imperative bits from this high-dimensional data set. The DR technique proposed here is a classifier-based fe

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