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The Accuracy of Electrocardiographic Criteria for Predicting Left Ventricular Hypertrophy in adult Patients with Systemic Hypertension
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ABSTRACT: BACKGROUND: Left ventricular hypertrophy is a significant risk factor for cardiovascular complications such as ischemic heart disease, heart failure, sudden death, atrial fibrillation, and stroke. A proper non-expensive tool is required for detection of this pathology. Different electrocardiographic (ECG) criteria were investigated; however, the results were conflicting regarding the accuracy of these criteria. OBJECTIVE: To assess the accuracy of three electrocardiographic criteria in diagnosis of left ventricular hypertrophy in adult patients with hypertension using echocardiography as a reference test. PATIENTS AND METHODS: This is a hospital-based cross sectional observational study which included 340 adult patients with a history of hypertension (240 patients with left ventricular hypertrophy and 100 patients without depending on Echocardiographic results). Three electrocardiographic criteria including Sokolow Lyon Voltage, Cornell voltage, and Cornell voltage duration were evaluated for their sensitivity and specificity in detection of left ventricular hypertrophy in those patients. RESULTS: Each of older ages (over 50 years) (OR= (OR=6.25, 95%CI=3.75-10.39, p<0.001), male gender (OR=0.58, 95% CI= 0.36-0.93, p= 0.018) and type 2 diabetes mellitus (OR=8.14, 95%CI= 4.04-16.41, p<0.001) were significantly associated with development of left ventricular hypertrophy in patients with hypertension. The sensitivity and specificity of Sokolow Lyon Voltage, Cornell voltage, and Cornell voltage duration were 17.5% and 96%; 13.33% and 97%; and 10% and 98%, respectively. CONCLUSION: Older ages, male gender, and type 2 diabetes mellitus can increase the risk of left ventricular hypertrophy in hypertensive patients. All the studied criteria have low sensitivity and high specificity in recognition of the left ventricular hypertrophy in patients with hypertension, with no advantage of definite criterion over the others.

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Economics And Administrative Sciences
Predicting Social Security Fund compensation in Iraq using ARMAX Model
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Time series have gained great importance and have been applied in a manner in the economic, financial, health and social fields and used in the analysis through studying the changes and forecasting the future of the phenomenon. One of the most important models of the black box is the "ARMAX" model, which is a mixed model consisting of self-regression with moving averages with external inputs. It consists of several stages, namely determining the rank of the model and the process of estimating the parameters of the model and then the prediction process to know the amount of compensation granted to workers in the future in order to fulfil the future obligations of the Fund. , And using the regular least squares method and the frequ

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Publication Date
Wed Jan 01 2014
Journal Name
Iraqi Journal Of Agricultural Sciences
Predicting maize ear grain weight in situ by ear dimensions
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To find out a simple and efficient equation to estimate maize ear grain weight on farm (in situ), twenty three maize crosses along with two synthetics were grown in the field. On the experimental farm of the Dept. of Field Crop Sci., College of Agric., Univ. of Baghdad, seeds of twenty five maize genotypes were grown in the fall season of 2013 with three replicates. At dough stage of the kernels, five naked ears of each experimental units were measured for length and maximum diameter. This will sum up 125 ears of the trial. The volumes of ears were calculated as cylinder (length× r2× 3.1416). Grain weight of all ears were determined after harvesting and drying to 15% grain moisture. A constant was calculated by dividing ear grain weight b

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Publication Date
Sun Mar 03 2013
Journal Name
Baghdad Science Journal
Effect of Methotrexate on the Liver Enzymes and Lipid Profile in Adult Female Albino Mice
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Methotrexate (MTX) was used for treatment of malignancies and now is widely used in treatment of rheumatoid arthritis. In this research the evaluation of the effects of MTX on some liver enzymes and lipid profile was studied. Twenty four adult female mice divided into three groups (8 mice each). The first two groups were treated with MTX while the third group was used as a control. MTX was intraperitoneally given at 50 µg/ml and 75 µg/ml to the first and second groups respectively for 35 days ,whereas the control group was intraperitoneally injected with normal saline. The results showed a significant (p<0.05) increase in serum levels of glutamic oxaloacetate transaminase (GOT) , glutamic pyruric transaminase (GPT), Alkaline pho

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Publication Date
Wed Nov 07 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
The Influence of Ramadan Fasting on Some Hematological and Biochemical Parameters in Healthy Adult Males
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Objective(s): Ramadan is the Holy month of the Muslims, where they are required to abstain from food and drinks
from dawn till the beginning of night. This study was conducted in Ramadan to investigate the effect of fasting on
hematological incidences, lipid profile, renal and liver function tests among healthy adult males.
Methodology: The present study was carried out in Ramadan – 1431 of Higira (August-September 2010). The study
sample was 56 healthy adult males. Five samples of blood were taken at five intervals (Before, at day 1, 15, 28 and
after Ramadan). Estimation was done for hematological markers, (hemoglobin, white blood cells count, platelet
count); renal function tests (blood urea, serum uric acid, serum

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Publication Date
Mon Jan 01 2024
Journal Name
2nd International Conference For Engineering Sciences And Information Technology (esit 2022): Esit2022 Conference Proceedings
Ideals of the full transformation semigroup of a free left G-act on n-generators
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Publication Date
Sun Jan 10 2016
Journal Name
British Journal Of Applied Science &amp; Technology
The Effect of Classification Methods on Facial Emotion Recognition ‎Accuracy
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The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

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Publication Date
Wed Jun 01 2022
Journal Name
International Dental Journal
A Survey of Systemic Antibiotic Prescription Patterns Amongst Iraqi Dentists
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Publication Date
Wed Jan 01 2020
Journal Name
Methods And Objects Of Chemical Analysis
Predicting The Composition Of Qurna Crude Oil Fraction By Ternary Composition Diagram
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With a goal to identify, and ultimately removing from the oil fraction, the carcinogenic components, an oil fraction oil has been analyzed into a main three hydrocarbon groups, paraffins, aromatics, and polycyclic saturates. A multi-stage adsorption apparatus has been used. Four units of 300 g alumina each seems to be sufficient for removing the polynuclear aromatics from 75 g of an oil fraction boiling between 365-375 °C from Qurna crude oil. The usefulness of the ternary diagram for analyzing the oil fraction to the three hydrocarbons groups has been studied and verified. An experimentally based linear relationship of density and refractive index was established to enable of identifying the composition of an oil fraction using th

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Publication Date
Sat Aug 09 2025
Journal Name
Scientific Reports
Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.)
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Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

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Publication Date
Sun Mar 17 2019
Journal Name
Baghdad Science Journal
A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures
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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

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