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Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques
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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 a training data rather than cross validation. The decision tree algorithm J48 is applied to detect and generate the pattern of attributes, which have the real effect on the class value. Furthermore, the experiments are performed with three machine learning algorithms J48 decision tree, simple logistic, and multilayer perceptron using 10-folds cross validation as a test option, and the percentage of correctly classified instances as a measure to determine the best one from them. As well as, this investigation used the iteration control to check the accuracy gained from the three mentioned above algorithms. Hence, it explores whether the error ratio is decreasing after several iterations of algorithm execution or not. Conclusion It is noticed that the error ratio of classified instances are decreasing after 5-10 iterations, exactly in the case of multilayer perceptron algorithm rather than simple logistic, and decision tree algorithms. This study realized that the TPS_pre is the most common effective attribute among three main classes of examined dataset. This attribute highly indicates the BC inflammation.

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Publication Date
Sat Apr 01 2017
Journal Name
Diyala Journal For Pure Science
Determination of IL-35, PTH, Ferritin and Other biochemical parameters in sera of Iraqi Men with chronic kidney failure.
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Chronic kidney Failure, a progressive disease, includes both medical and biochemical features that damage kidneys and decrease their abilities to work effectively, this disease is characterized by a chronic disorders to both the innate and adaptive immune systems, generate a complex and not fully understood immune dysfunction. In the present study, (30) men suffering from chronic kidney failure with age in range (40-55) year and (30) healthy men within the same range of age were enrolled in this study. The aim of this study is to highlight the role of immunological aspect (IL-35), hormonal aspects (PTH), some functional proteins and immunological electrolytes in sera of chronic kidney failure (CKF) patients. Biochemical parameters were dete

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Publication Date
Fri Apr 30 2021
Journal Name
Onkologia I Radioterapia
The prevalence of lymph proliferative disorders in a group of Iraqi patients and its relation to blood indices parameters
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Abstract: Lymphoproliferative Disorders (LPDs) are a group of neoplasms affecting various cells within lymphoid system. Each type has different treatment a..70619

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Publication Date
Wed Jan 01 2020
Journal Name
Annals Of Tropical Medicine And Public Health
Determination of hepcidin (25) level in hemodialysis Iraqi male patients and its relation with erythropoietin and some biochemical parameters
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Background: Anemia is a common finding in patients with renal failure disease and it is, primarily, due to reduced production of iron and erythropoietin. Therefore, the aim of current study was to evaluate the relationship between serum level of hepcidin 25, on the one hand, and levels of serum iron, total iron-binding capacity, serum erythropoietin and transferrin saturation% in Iraqi male patients undrgoning haemodialysis.. Methods: Serum from75 males in the end stage of hemodialysis, as group one, and 25 Iraqi healthy malecontrols, as group two, were recruited in this study. Results: Data from current study showed significant increase in hepcidin 25 and significant decrease in serum iron, total iron-binding capacity, transferrin saturati

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Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
New Robust Estimation in Compound Exponential Weibull-Poisson Distribution for both contaminated and non-contaminated Data
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Abstract

The research Compared two methods for estimating fourparametersof the compound exponential Weibull - Poisson distribution which are the maximum likelihood method and the Downhill Simplex algorithm. Depending on two data cases, the first one assumed the original data (Non-polluting), while the second one assumeddata contamination. Simulation experimentswere conducted for different sample sizes and initial values of parameters and under different levels of contamination. Downhill Simplex algorithm was found to be the best method for in the estimation of the parameters, the probability function and the reliability function of the compound distribution in cases of natural and contaminateddata.

 

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Publication Date
Tue Jan 01 2019
Journal Name
Indian Journal Of Public Health Research & Development
Impact of Breast Feeding Duration on the Presence of <i>Candida</i> in Relation to Nonnutritive Sucking Habit among Group of Iraqi Children
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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Petroleum Science And Engineering
Performance evaluation of analytical methods in linear flow data for hydraulically-fractured gas wells
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Publication Date
Tue Jan 01 2019
Journal Name
Baghdad Science Journal
Hazard Rate Estimation Using Varying Kernel Function for Censored Data Type I Article Sidebar
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n this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used: local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the types of the kernel boundary func

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Publication Date
Fri Jan 01 2016
Journal Name
Statistics And Its Interface
Search for risk haplotype segments with GWAS data by use of finite mixture models
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The region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled

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Publication Date
Wed Sep 23 2020
Journal Name
Artificial Intelligence Research
Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set
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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

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