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.
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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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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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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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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The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri
... Show MoreThe purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
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In the present time, radioactive contamination is considered one of the most dangerous types of environmental pollution. It usually takes place because of a leakage of radioactive materials to one of the environment natural components, such as, water, air, and soil. Iraq is considered one of the most contaminated environments in the world; this is closely associated with the wars Iraq had suffered from; especially, in 1991 and 2003. Considering the importance of the radioactive contamination and its different health impacts on the population, the current paper is interested in studying this type of environmental contamination and its impact on the birth defects depending on the data available in the annual reports issued by the Iraqi min
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