Diabetes imposes a substantial public health burden; according to the International Diabetes Federation, there were about 3.4 million diabetes related deaths worldwide in 2024, and in Iraq, the Federation reports that one in nine adults lives with diabetes in 2024, with 14,683 adult deaths attributable to diabetes and a total diabetes related health expenditure of 2,078 million United States dollars. The dataset analyzed in this study contains 1,000 records collected in 2020 from two Iraqi teaching hospitals and includes multiple clinical and laboratory measurements with three outcome classes, namely Non diabetic, Pre diabetic, and Diabetic, with a low prevalence of the Pre diabetic class and an imbalanced overall class distribution; the data are challenging because they contain many outliers, non homogeneous covariance matrices across classes, exact duplicate rows that were removed before modelling, and linear correlations among certain variables. The study objective was to train and evaluate models that discriminate among the three classes and yield accurate, well calibrated predictions for future cases in similar clinical settings, but the diagnostic properties of the data limited the applicability of classical discriminant functions; therefore two supervised learners were employed: Classification and Regression Trees (CART) and Extreme Gradient Boosting (XGBoost), together with preprocessing that removed exact duplicate rows and excluded VLDL because it is algebraically derived from triglycerides in mmol per liter as VLDL equals triglycerides divided by 2.2, which would introduce redundancy and multicollinearity. On the heldout test set, XGBoost achieved higher Accuracy at 98.18 percent compared with 97.58 percent for CART and higher Balanced Accuracy at 93.84 percent compared with 88.16 percent for CART, indicating that XGBoost provided the strongest overall operating point for this three-class task while CART remains useful when simple and transparent rules are required.
In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used
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In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.
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Type 2 diabetes mellitus (DM) is a group of metabolic disorder disease. The inflammatory markers act as a new risk factor for development of type 2 diabetes with a possible association with ABO/Rh blood groups. Human ABO genes are located on chromosome 9q34.1-q34.2. The aim of this study was to investigate the possible association between inflammatory markers, interleukin (IL) -18 and IL-33 in type 2DM and ABO blood groups. Sixty four patients with newly diagnosed type2 DM and control group consist of twenty healthy Iraqi individual. Laboratory test were include ABO blood groups using standard serological procedures and detection IL-18 and IL-33 in serum by ELISA kits. The Present data showed a significant increase i
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Objectives: Teenage pregnancy with gestational diabetes mellitus (GDM) offers a real challenge to the health system and needs a special care. We aimed to evaluate possible obstetrical and neonatal adverse events of different treatment protocols in adolescent GDM including lifestyle, metformin (MTF), and insulin. Methods: All teen pregnant women ≤ 19 years old visiting Baghdad Teaching Hospital throughout four years (from June 1, 2016 till May 31, 2020) diagnosed with GDM were included in this cohort study and followed-up closely throughout pregnancy and after delivery. Included adolescents were put on lifestyle alone during the first week of presentation. Adolescents who reached target glucose measurements were categorized i
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Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental
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Type 1 diabetes mellitus (T1DM) is an autoimmune disease frequently associated with autoimmune thyroid disease (AITD). The study is conducted at the Specialized Center for Endocrinology and Diabetes-Baghdad at Al-karkh side, during December 2013 up to April 2014. In this study, we investigate the prevalence of anti-thyroid peroxidase (anti-TPO) antibody in(80) type1 diabetic patients with (AITD) and (30) healthy controls .Blood samples are taken for investigation of thyroid tests by using Vitek Immunodiagnstic Assay System (VIDAS).Enzeme Linked Immunosorbent Assay (ELISA) is used to detect anti-thyroid antibody(anti-TPO). The results show that age, gender and BMI (body mass index) are similar in both groups, p>0.05. Among 80 type1 diabetic
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In diabetes, impaired wound healing and other tissue abnormalities are considered major concerns. Many factorsaffect the time and quality of wound healing. One of the purposes of medical sciences is wound healing in a short time withreduced side effects. The herbal products are more precious in both prophylaxis as well as curative in delayed diabetic woundhealing activity when compared to synthetic drugs.A wide range of evidence has shown that capers plant possesses differentbiological effects, including antioxidant, anticancer and antibacterial effects. Phytochemical analysis shows thatC. spinosahashigh quantities of bioactive constituents, including polyphenolic compounds, which are responsible for its health-promotingeffects. The healing
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