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Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver operating characteristic ROC).   Dataset was downloaded from UCI ml repository; it is composed of 9 attributes and 699 samples. The findings are clearly showing that the RBF NN classifier is the best in prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC (area under Receiver Operating Characteristic ROC) among all other models.</p>

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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 Jun 22 2021
Journal Name
Expert Systems
Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks

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Publication Date
Mon Jan 30 2023
Journal Name
Iraqi Journal Of Science
Cytotoxic Effect of the Alcoholic Extract of Conocarpus erectus Leaves on MDA-MB 231 and MCF7 Breast Cancer Cell Lines

    Currently, there is a growing interest in medicinal plants extracts as some plants have shown antitumor potential. The goal of this study was to test the anticancer activity of methanol extract of Conocarpous erectus leaves in breast cancer cells.  Cytotoxicity was tested in vitro on breast cancer cell lines, MCF7 [Estrogen receptor + (ER+)] and MDA-MB231 [Estrogen receptor - (ER-)], in addition to normal fibroblast cells (REF). MTT assay was utilized to measure the growth inhibitory effects after 48 hours exposure to extracts.  Viability results indicated  that MDA-MB231 were sensitive (GI50 = 56.1µg/ml).However, no sensitivity was seen in both MCF7 and REF cells (GI50>100 µg/ml). I

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Publication Date
Fri Jun 01 2018
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Performance assessment of biological treatment of sequencing batch reactor using artificial neural network technique.

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

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Publication Date
Tue Oct 01 2019
Journal Name
International Journal Of Pharmaceutical Research
Study of aminoacy lt ran-synthetasecomplex interacting multifunctional protein 1 and liver enzymes in iraqi women with breast cancer undergoing chemotherapy

Breast carcinoma is one of the greatest popular neoplasms in females. It is a major reason of demise in the world, and it is the first cancer in ranking diagnosed in Iraqi women. This study aimed to determine aminoacyltRAN-synthetase complex interacting multifunctional protein 1 and liver enzymes levels in Iraqi females with stage II breast malignance, and study the effect of chemotherapy (after surgery) on these markers. This study included 50 females patients with stage II breast malignance (before and after surgery and second dose of chemotherapy) attending the Oncology Teaching Hospital in Medical City/ Baghdad, in addition to 20 persons as controller group were chosen without any chronic diseases. Their ages ranged from (30-55) years.

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction

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

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Publication Date
Fri Dec 24 2021
Journal Name
Oncology And Radiotherapy
The effect of different clinicopathological parameters on disease free survival in triple negative breast cancer Iraqi women

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Publication Date
Fri Apr 30 2021
Journal Name
Eastern-european Journal Of Enterprise Technologies
Implementation of artificial neural network to achieve speed control and power saving of a belt conveyor system

According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through

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Publication Date
Sat Jun 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of some methods for estimating the parameters of the binary logistic regression model using the genetic algorithm with practical application

Abstract

   Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model

    In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe

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Publication Date
Sun Apr 03 2016
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Imaging and clinicopathological characteristics of breast cancer among women under the age of 40 years

Background: although breast cancer in young women is less common and often overlooked, it is still considered a major health concern.
Objectives: to evaluate the demographic, clinical, radiological and histopathological characteristics of breast cancer among a sample of Iraqi women diagnosed under the age of 40 years.
Patients and methods: a retrospective study enrolled 73 females below the age of 40 years with a history of breast cancer. All data was extracted from an established information system database designed by the Principal Investigator of the Iraqi National Breast Cancer Research Project under supervision of the International Agency for Research on Cancer (IARC) over a 4-years period from 2011 to 2014.
Results: sevent

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