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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
Tue Dec 06 2022
Journal Name
Iraqi National Journal Of Nursing Specialties
Evaluation of Blended Learning in Nursing Education at the Middle Region in Iraq
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Abstract

Objective(s): To evaluate blended learning in nursing education at the Middle Region in Iraq.

Methodology: A descriptive study, using evaluation approach, is conducted to evaluate blended learning in nursing education in Middle Region in Iraq from September 26th, 2021 to March 22nd, 2022. The study is carried out at two Colleges of Nursing at the University of Baghdad and University of Tikrit in Iraq. A convenient, non-probability, sample of (60) undergraduate nursing students is selected. The sample is comprised of (30) student from each college of nursing, Self-report questionnaire is constructed from the literature, for e

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Publication Date
Sat Jun 30 2012
Journal Name
Al-kindy College Medical Journal
The Detection of Silent Celiac Disease In patients With Type 1 Diabetes Mellitus by the use of Anti Tissue Transglutaminase Antibodies
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Objective: Detection the presumptive prevalence of
silent celiac disease in patients with type 1 diabetes
mellitus with determination of which gender more
likely to be affected.
Methods: One hundred twenty asymptomatic patients
[75 male , 45 female] with type 1 diabetes mellitus
with mean age ± SD of 11.25 ± 2.85 year where
included in the study . All subjects were serologically
screened for the presence of anti-tissue transglutaminase
IgA antibodies (anti-tTG antibodies) by Enzyme-
Linked Immunosorbent Assay (ELISA) & total IgA
was also measured for all using radial
immunodiffusion plate . Anti-tissue transglutaminase
IgG was selectively done for patients who were
expressing negative anti-

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Publication Date
Sun Dec 30 2012
Journal Name
Al-kindy College Medical Journal
The Detection of Silent Celiac Disease In patients With Type 1 Diabetes Mellitus by the use of Anti Tissue Transglutaminase Antibodies
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Objective: Detection the presumptive prevalence of silent celiac disease in patients with type 1 diabetes mellitus with determination of which gender more likely to be affected.
Methods: One hundred twenty asymptomatic patients [75 male , 45 female] with type 1 diabetes mellitus with mean age ± SD of 11.25 ± 2.85 year where included in the study . All subjects were serologically screened for the presence of anti-tissue transglutaminase IgA antibodies (anti-tTG antibodies) by Enzyme-Linked Immunosorbent Assay (ELISA) & total IgA was also measured for all using radial immunodiffusion plate . Anti-tissue transglutaminase IgG was selectively done for patients who were expressing negative anti-tissue transglutaminase IgA with low tot

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Publication Date
Mon Feb 14 2022
Journal Name
Journal Of Educational And Psychological Researches
Cognitive Beliefs and Their Relationship to Self-Organized Learning Strategies at the Preparatory Stage
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The study aims to identify the level of cognitive beliefs, as well as to identify the level of self-organized learning strategies among intermediate school students. The study also aims to identify the differences in the level of self-organized learning strategies among intermediate school students in term of gender, branch (scientific, literary). In order to achieve the research objectives, the researcher designed a scale to measure the cognitive beliefs. As for the scale of self-organized learning strategies, the researcher adopted a scale of (Pintrich et al. 1991), which was translated by (Izzat Abdelhamid, 1999) , For self-organized learning strategies, the sample consisted of (400) students from the research population, whic

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Publication Date
Tue Nov 21 2017
Journal Name
Lecture Notes In Computer Science
Emotion Recognition in Text Using PPM
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In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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Publication Date
Mon Apr 01 2024
Journal Name
Journal Of Advanced Pharmaceutical Technology & Research
Effects of subclinical hypothyroidism in type II diabetes mellitus patients on biochemical, coagulation, and fibrinolysis status
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The aim of the currnet study to examine the effect of subclinical hypothyroidism (SCH) in diabetic patients on coagulation parameters. This retrospective case–control study involves 130 patients diagnosed with type 2 diabetes mellitus (T2DM), divided into 65 T2DM with newly diagnosed SCH and 65 euthyroid (EUT) T2DM patients without SCH. Fibrinogen (FIB) was significantly higher in SCH (508.2 ± 63.0 mg/dL) than EUT (428.1 ± 44.8 mg/dL). In the SCH patients, FIB correlated with several parameters, such as age (β = 0.396), body mass index (β = 0.578), glycated hemoglobin (β = 0.281), and activated partial thromboplastin time (β = 0.276). In conclusion SCH in DM patients appears to increase the magnitude of coagulopathy.

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Publication Date
Thu May 04 2023
Journal Name
Journal Of Indian Association Of Biomedical Scientists
“Relationship between Cathepsin K and Total oxidative state in diabetes mellitus Iraqi Women patients with osteoporosis
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Introduction and Aim: Diabetes mellitus patients almost always struggle with a metabolic condition known as chronic hyperglycemia. According to the World Health Organization, osteoporosis is a progressive systemic skeletal disorder that is characterized by decreasing bone mass and microstructural breakdown of bone tissue that increases susceptibility to fracture and increased risk of breaking a bone. Here, we aimed to compare the levels of CatK and total oxidative state in patients with diabetes and osteoporosis among the female Iraqi population and study the possible relationship between them. Materials and Methods: This study included 40 females with diabetes (Group G1), 40 with diabetes and osteoporosis (Group G2) and 40 normal healthy f

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Publication Date
Wed Sep 01 2021
Journal Name
Medical Journal Of Babylon
Serum Ghrelin Level in Type 2 Diabetes Mellitus Postmenopausal Women in Relation to Body Mass Index
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Publication Date
Fri Mar 31 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Effects of Allopurinol on Ketone Body Metabolism and Tissue Lipid Peroxidation in Alloxan Diabetes in Rats
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The aim of the present study is to investigate whether or not xanthine oxidase (XO)–derived reactive oxygen species (ROS) may play a role in the pathogenesis of alloxan (ALX)–induced diabetes in rats using the specific XO inhibitor and hydroxyl radical scavenger, allopurinol

The involvement of oxidative stress in ALX – diabetes was assessed by the measurement of plasma and various tissues lipid peroxides levels ( using thiobarbituric acid ( TBA ) reactive substances ). Furthermore, the ability of allopurinol to influence these and other biochemical parameters, including plasma and urine ketones levels were also investigated in diabetic rats.

Rats were divided into four groups: control, untreated diabe

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