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.
The present study investigated the use of pretreated fish bone (PTFB) as a new surface, natural waste and low-cost adsorbent for the adsorption of Methyl green (MG, as model toxic basic dye) from aqueous solutions. The functional groups and surface morphology of the untreated fish bone (FB) and pretreated fish bone were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM) and Energy dispersive X-ray spectroscopy (EDS),respectively. The effect of operating parameters including contact time, pH, adsorbent dose, temperature, and inorganic salt was evaluated. Langmuir, Freundlich and Temkin adsorption isotherm models were studied and the results showed that the adsorption of basic dye followed Freundlich iso
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This study is carried out on patients with type 2 diabetes mellitus to assess the lipid profile, malondialdehyde and glutathione. Our study is concerned with 51 (Iraqi Arab females) patients of type 2 diabetes mellitus compared with 31 control subjects unified in age, sex and ethnic background. Lipid profile is measured by using commercially available kits, while the serum MDA and glutathione levels are measured by means of sandwich ELISA test using commercially available kits. Serum MDA is significantly higher (P<0.001) while glutathione is significantly lower (P<0.001) in type 2 diabetic patients when compared to the control. The normal levels of MDA (3.82 ± 0.77n mol/ml) and GSH (2.23 ± 0.54 µg/ml) recorded for the non-diabetic female
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Inflammasome is a multiprotein oligomer complex which is the precursor procaspase-1 and is a component of the innate immune system so that this study aimed to determine the serum levels of interleukin-1? and 18 in patients with T2D and simple obesity in an attempt to clarify the role of inflammasome in these disorders.Twenty five known cases of T2D, twenty four patients with simple obesity and twenty healthy subjects were randomly recruited from AL-Kindy Teaching Hospital in Baghdad to enroll in this study. All the data about the demographic characteristics and anthropometric measurements were determined in all patients, also the C-reactive protein and serum interleukin (IL)-1? and IL-18 levels were obtained from each patient. The results s
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To determine the relationship between hepatitis C virus infection and Diabetic mellitus type 2 , twenty patient's with diabetic mellitus type 2 aged (30-61) years old have been investigated from 01/11/2014 to 01/02/2015 and compared with fifteen parentally healthy individuals. All the studies groups were carried out to measure anti-HCV Abs by enzyme linked immunosorbent assay (ELISA), There was significant elevation (P≤0.05) in the HCV Abs compared with control groups .The percentage of HCV Abs was 15% and there was highly significant (P≤0.01) differences between studied group, while there was non-significant differences (P≥0.05) between patients groups according to age and gender compared with control groups. These results indicated
... Show MoreThe existing study aimed to assess four soil moisture sensors’ capacitive (WH51 and SKU: S EN0193) and resistive (Yl69 and IC Station) abilities, which are affordable and medium-priced for their accuracy in six common soil types in the central region of Iraq. The readings’ calibration for the soil moisture sensor devices continued through two gravimetric methods. The first depended on the protocols’ database, while the second was the traditional calibration method. The second method recorded the lowest analysis error compared with the first. The moderate-cost sensor WH51 showed the lowest standard error (SE), MAD , and RMSE and the highest R² in both methods. The performance accuracy of WH51 was close to readings shown by the manufac
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Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.
Change detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac
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Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica
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