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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
Sat Nov 10 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Effectiveness of Instructional Intervention on Medical and Health Information of Patients with Diabetes Mellitus Type II
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Objective: The objectives of the present study were to evaluate the effectiveness of the instructional intervention
about medical and health knowledge of patients with diabetes mellitus type II.
Methodology: A Quasi- experimental study was carried out in National Center for Diabetes Mellitus/ Almustansria
University, started from 4th January 2012, to 1st April 2012. Non-probability (purposive sample) of (50) diabetes
mellitus type II, who visit National Center for Diabetes Mellitus/ Almustansria University. The study sample is
divided equally into (25) study and (25) control groups. The study group received the instructional intervention.
While the control not exposed to the instructional intervention. The data are coll

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Publication Date
Thu Dec 31 2015
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Performance Equations for Household Compressors Depending on Manufacturing Data for Refrigerators and Freezers
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Abstract

 A surface fitting model is developed based on calorimeter data for two famous brands of household compressors. Correlation equations of ten coefficient polynomials were found as a function of refrigerant saturating and evaporating temperatures in range of (-35℃ to -10℃) using Matlab software for cooling capacity, power consumption, and refrigerant mass flow rate.

Additional correlations equations for these variables as a quick choice selection for a proper compressor use at ASHRAE standard that cover a range of swept volume range (2.24-11.15) cm3.

The result indicated that these surface fitting models are accurate with in ± 15% for 72 compressors model of cooling cap

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Publication Date
Mon Oct 08 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
TOTAL ORGANIC CARBON (TOC) PREDICTION FROM RESISTIVITY AND POROSITY LOGS: A CASE STUDY FROM IRAQ
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     The open hole well log data (Resistivity, Sonic, and Gamma Ray) of well X in Euphrates subzone within the Mesopotamian basin are applied to detect the total organic carbon (TOC) of Zubair Formation in the south part of Iraq. The mathematical interpretation of the logs parameters helped in detecting the TOC and source rock productivity. As well, the quantitative interpretation of the logs data leads to assigning to the organic content and source rock intervals identification. The reactions of logs in relation to the increasing of TOC can be detected through logs parameters. By this way, the TOC can be predicted with an increase in gamma-ray, sonic, neutron, and resistivity, as well as a decrease in the density log

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Publication Date
Thu Mar 30 2023
Journal Name
مجلة الحقيقة
University e-learning and its role in raising technological skills
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تتبلور فكرة البحث حول التوصل لنوع العلاقة التي تربط التعليم الالكتروني خلال جائحة كورونا برفع المهارات التكنولوجية للأساتذة والطلاب، وتبرز أهمية البحث في ان نجاح الوصول لهذه العلاقة يمكن الإفادة منها في تغيير منهجية تطوير المهارات التكنولوجية مستقبلا وذلك باعتماد الجوانب التطبيقية الفعلية بدلا من الدورات وورش العمل والتي قد لا تضاهي الطريقة العملية في رفع مستوى المهارات المختلفة سواء التدريسية او التكنو

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Publication Date
Wed Mar 23 2011
Journal Name
Journal Of Baghdad College Of Dentistry
Factors associated with parotid gland enlargement among poorly controlled Type II Diabetes Mellitus
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Background: Microscopic examination of parotid gland reveals hypertrophy of the aciner cells sometimes two to three times greater than normal size of PG, in cases associated with longstanding diabetes. This study was designed to determine the effects of duration, fasting plasma glucose and glycosylated hemoglobin on parotid gland enlargement among poorly controlled type 2 diabetes mellitus. Subjects, Materials, and Method: This study was conducted on 36 parotid glands of 18 with type 2 DM , at age range ( 40-60) years, all of them were selected from subjects attending (Endocrine clinic for diabetic patients) in Baghdad Teaching Hospital. , pg was measured with ultrasonography in both longitudinal and horizontal plane. Results: the rate of e

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Pharmaceutical Sciences & Research
Determination of Some Biochemical Parameters of Acromegalic Patients with Type 2 Diabetes Mellitus.
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Background: Because of the disturbance in the pituitary gland, growth hormone (GH) secretion will be increased and, as a result, insulin-like growth factor 1 (IGF-1) secretion will be increase as well, leading to a chronic and rare disease called acromegaly disease. One of the most serious complications of acromycaly is diabetes. Insulin resistance, which causes diabetes, occurs in the body because of increased growth hormone secretion Objective: The aim of this work is to estimate some biochemical parameters. These parameters were not studied extensively in the literature such as BALP and LOX and the possibility of using LOX as a new biomarker for acromyalgic patients with diabetic. Patients and Methods: The study was performed on (25) mal

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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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Publication Date
Thu Jun 01 2017
Journal Name
Applied Nursing Research
Examining perceived and actual diabetes knowledge among nurses working in a tertiary hospital
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Publication Date
Mon Oct 19 2020
Journal Name
International Journal Of Psychosocial Rehabilitation
The effect of using the interactive video accompanying the static training in learning some basic skills of a model school in squash
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Lately great interests have emerged to find educational alternatives to teach and improve motor skills according to modern educational methods that take into account individual differences and speed in learning for the learner through individual learning that the learner adopts by teaching himself by passing through various educational situations to acquire skills and information in the way he is The learner is the focus of the educational process and among these alternatives the interactive video, the researchers noted through the educational training units at the Model Squash School of the Central Union, and that most of the methods and methods used in learning basic skills take a lot of time in the educational program and do not involve

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Publication Date
Thu Nov 03 2022
Journal Name
Sensors
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
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In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

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