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Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five attributes of the training process. The results of the second experiment showed improvement in the performance of the KNN and the Multilayer Perceptron. The results of the second experiment showed a slight decrease in the performance of the Random Forest with 97.5 % accuracy.

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Publication Date
Sun Sep 04 2011
Journal Name
Baghdad Science Journal
The Prediction of the Electromagnetic Properties and the ?(E2/M1) of 110-116Cd-Isotopes in IBM Model
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The Nuclear structure of 110-116Cd isotopes was studied theoretically in the framework of the interacting boson model of IBM-l and IBM-2. The properties of the lowest mixed symmetry states such as the 1+, 2+ and 3+ levels produced by the IBM-2 model in the vibrational-limit U(5) of Cd - isotopes are studied in details. This analysis shows that the character of mixed symmetry of 2+ is shared between and states in 110-114Cd – isotopes, the large shar goes to s, while in isotope, the state is declared as a mixed symmetry state without sharing. This identification is confirmed by the percentage of F-spin contribution. The electromagnetic properties of E2 and Ml operators were investigated and the results were analyzed. Various

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Publication Date
Wed Dec 05 2018
Journal Name
Italian Journal Of Gynaecology & Obstetrics
Prediction of Fetal Lung Maturity by Ultrasonic Thalamic Echogenicity and Ossification Centers of Fetal Femur and Tibia
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Engineering
A Prediction Formula for The Estimation of Sediment Load in The Upper Reach of Al-Gharraf River
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The presence of deposition in the river decreases the river flow capability's efficiency due to the absence of maintenance along the river. In This research, a new formula to evaluate the sediment capacity in the upstream part of Al-Gharraf River will be developed. The current study reach lies in Wasit province with a distance equal to 58 km. The selected reach of the river was divided into thirteen stations. At each station, the suspended load and the bedload were collected from the river during a sampling period extended from February 2019 till July 2019. The samples were examined in the laboratory with a different set of sample tests. The formula was developed using data of ten stations, and the other three s

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Hydraulic Flow Units for Jeribe Reservoir in Jambour Oil Field Applying Flow Zone Indicator Method
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The Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units.    This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized poros

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Publication Date
Sat Dec 01 2018
Journal Name
Al-khwarizmi Engineering Journal
Two Domain Flow Method for Leachate Prediction Through Municipal Solid Waste Layers in Al–Amari Landfill Site
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Existing leachate models over–or underestimates leachate generation by up to three orders of magnitude. Practical experiments show that channeled flow in waste leads to rapid discharge of large leachate volumes and heterogeneous moisture distribution. In order to more accurately predict leachate generation, leachate models must be improved. To predict moisture movement through waste, the two–domain PREFLO, are tested. Experimental waste and leachate flow values are compared with model predictions. When calibrated with experimental parameters, the PREFLO provides estimates of breakthrough time. In the short term, field capacity has to be reduced to 0.12 and effective storage and hydraulic conductivity of the waste must be increased to

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Publication Date
Wed Dec 01 2021
Journal Name
International Journal Bioautomation
Model for Prediction of the Weight and Height Measurements of Patients with Disabilities for Diagnosis and Therapy
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Background: Accurate measurement of a patient’s height and weight is an essential part of diagnosis and therapy, but there is some controversy as to how to calculate the height and weight of patients with disabilities. Objective: This study aims to use anthropometric measurements (arm span, length of leg, chest circumference, and waist circumference) to find a model (alternatives) that can allow the calculation of the height and the body weight of patients with disabilities. Additionally, a model for the prediction of weight and height measurements of patients with disabilities was established. Method: Four hander patients aged 20-80 years were enrolled in this study and divided into two groups, 210 (52.5%) male and 190 (47.5%) fe

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Publication Date
Mon Oct 02 2023
Journal Name
Journal Of Engineering
Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking
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This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

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Publication Date
Sun May 12 2013
Journal Name
International Journal Of Pharmacy And Pharmaceutical Sciences
PLEIOTROPIC EFFECTS OF ATORVASTATIN ON PRO-INFLAMMATORY CYTOKINES IN NEWLY DIAGNOSED IRAQI PATIENTS WITH TYPE 2 DIABETES MELLITUS
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Objective: Atorvastatin therapy is now recommended for reduction of cardiovascular risk in type 2 diabetic patients (T2DM), based on convincing evidence of reductions in mortality and vascular events in major clinical outcome trials. The aim is to evaluate the effects of atorvastatin on proinflammatory markers (TNF-α, IL-6), HbA1c andleptin in obese patients with type 2 diabetes. Methods: Sixty fivenewly diagnosed T2DM patients were randomly allocated into 2 groups; group I treated with metformin only; in group II atorvastatin was added with metformin. Twenty healthy subjects were enrolled as control group. While maintaining their usual eating habits, fasting blood samples were collected at baseline and after 12 weeks of treatment. Results

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Publication Date
Thu Oct 20 2022
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A study of Hepcidin levels and other Biochemical parameters in woman with Osteoporosis with Type 2 Diabetes Mellitus
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Background: Diabetes mellitus (DM) could be regarded as a set of chronic metabolic disorders which have a common aspect of hyperglycemia. The resistance in the peripheral actions of insulin or impaired insulin secretion could be the reason  hepcidin which is a peptide hormone derived from liver, in systemic iron homeostasis is an essential regulator, and its lopsided production participates in the pathogenesis of iron disorders in spectrum. Osteoporosis often accompanies many diseases like ß-thalassemia, hemochromatosis, sickle liver diseases, cell disease and hemosiderosis featured by iron overload, evidences suggest that Iron overload and iron deficiency are suggested by evidences that they affect bone in a negative way, acting

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Association of Endothelin-I and A symmetric Dimethylarginine Levels with Insulin Resistance in Type-2 Diabetes Mellitus Patients
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Endothelin-I (ET-I) is one of the potent vasoconstrictors secreted from endothelial cells when needed. Many studies revealed the elevation of serum ET-I with human diabetes and microangiopathies. Since insulin resistance is a case of mixed diabetic and pre-diabetic cases, many risk factors beyond obesity and inflammation are proposed. The current study aims to demonstrate the association between serum ET-I and asymmetric dimethylarginine (ADMA) and insulin resistance in type 2 diabetes mellitus (T2DM). Sera of 73 subjects were enrolled currently (control= 35 subjects, and 38 with T2DM for more than 7 years), aged (40-60) years old, with distinct body mass index (BMI) ≤ 25 for control volunteers and (BMI) ≥ 25 for obesity and diabetes

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