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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
Sun Jan 03 2016
Journal Name
Journal Of The Faculty Of Medicine Baghdad
The Impact of Body Mass Index and Some Trace Elements in Iraqi Women with Breast Cancer

Background: Breast cancer is a highly heterogeneous disease globally. Trace elements such as copper and zinc have a role in many biochemical reactions as micro source, their metabolism is profoundly altered in neoplastic diseases especially breast cancer which is ranked as the first of female cancers
Objective: The aim of the present study is to study the impact of body mass index and some trace elements in Iraqi women with breast cancer.
Patients and methods: The group of the study consisted of 25 breast cancer patients; their age range was (25–65) years recruited from the Al-Kadhimia Teaching Hospital and 25 apparently healthy women age matched, over a period of 6 months from January 2015 until June 2015. After the diagnosis wa

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Publication Date
Sun Jan 02 2011
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Correlation of interleukin 6 (IL-6) with estrogen and progesterone receptor expression in breast cancer patients

Background: Multifunctional cytokines play important and only partially defined roles in mammary tumor development and progression. Normal human mammary epithelial cells constitutively produce interleukin 6(IL-6) and a non-secreted form of tumor necrosis factor. Transformation of mammary epithelial cells by different oncogenesis is frequently associated with alterations of cytokine/ growth factor production and responsiveness.
Methods: We measured levels of 1L-6 in 84 females with breast cancer and examined their correlation with clinicopathological variables including stages of the disease and estrogen and
progesterone receptor expression on tumor cell.
Results: Our results revealed significantly higher

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Publication Date
Mon Mar 23 2020
Journal Name
International Journal Of Nanoscience
Gold Nanoparticles Synthesis Using Environmentally Friendly Approach for Inhibition Human Breast Cancer

In this study, gold nanoparticles were synthesized in a single step biosynthetic method using aqueous leaves extract of thymus vulgaris L. It acts as a reducing and capping agent. The characterizations of nanoparticles were carried out using UV-Visible spectra, X-ray diffraction (XRD) and FTIR. The surface plasmon resonance of the as-prepared gold nanoparticles (GNPs) showed the surface plasmon resonance centered at 550[Formula: see text]nm. The XRD pattern showed that the strong four intense peaks indicated the crystalline nature and the face centered cubic structure of the gold nanoparticles. The average crystallite size of the AuNPs was 14.93[Formula: see text]nm. Field emission scanning electron microscope (FESEM) was used to s

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Publication Date
Tue Mar 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
Semi-parametric regression function estimation for environmental pollution with measurement error using artificial flower pollination algorithm

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin

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Publication Date
Sun Jul 31 2022
Journal Name
Iraqi Journal Of Science
Deep Learning and Machine Learning via a Genetic Algorithm to Classify Breast Cancer DNA Data

       This paper uses Artificial Intelligence (AI) based algorithm analysis to classify breast cancer Deoxyribonucleic (DNA). Main idea is to focus on application of machine and deep learning techniques. Furthermore, a genetic algorithm is used to diagnose gene expression to reduce the number of misclassified cancers. After patients' genetic data are entered, processing operations that require filling the missing values using different techniques are used. The best data for the classification process are chosen by combining each technique using the genetic algorithm and comparing them  in terms of accuracy.

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
HYBRID CIPHERING METHOD BASED ON CHAOS LOGISTIC MAP AND FINGERPRINT INFORMATION

In modern era, which requires the use of networks in the transmission of data across distances, the transport or storage of such data is required to be safe. The protection methods are developed to ensure data security. New schemes are proposed that merge crypto graphical principles with other systems to enhance information security. Chaos maps are one of interesting systems which are merged with cryptography for better encryption performance. Biometrics is considered an effective element in many access security systems. In this paper, two systems which are fingerprint biometrics and chaos logistic map are combined in the encryption of a text message to produce strong cipher that can withstand many types of attacks. The histogram analysis o

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Publication Date
Sat Apr 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Image encryption algorithm based on the density and 6D logistic map

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are genera

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
Machine Learning Approach for New COVID-19 Cases Using Recurrent Neural Networks and Long-Short Term Memory

     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470

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Publication Date
Sat Apr 01 2023
Journal Name
Heliyon
A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

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Publication Date
Thu Aug 31 2023
Journal Name
Oncology And Radiotherapy
The role of breath holding technique on minimizing cardiac dose in left breast cancer irradiation in the adjuvant setting

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