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.
Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–od
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Background: Obesity and diabetes mellitus are the common health problems,and obesity is common cause of the insulin resistance. Aim of studv: Aim of the study is to find any correlation between obesity (insulin resistance) and type I diabetes in children. Patients and methods: This study included (40) children with type I diabetes, in addition to (40) children as control.The age of all studied groups ranged from (8-18) years.This study was attemted from Ibn AlBalady Hospital during from 20 August to 9 Novembar,2008. The subjects wrer divided into (4) groups according to their BMI:- * Obese children,diabetes,n=2O,BMI>30. * Non obese children, diabetes, n=20,BMI<25. Obese children, non diabetes, n=20,BMI>30. * Non obese children,non diabetes
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, t
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Background: Atherosclerosis is well known related to age and certain cardiovascular diseases. Aging is one reason of arteries function deterioration which can cause loss of compliance and plaque accumulation, this effect increases by the presence of certain diseases such as hypertension and diabetes disease. Aim: To investigate the reduction of blood supply to the brain in patients with diabetes and hypertension with age and the role of resistive index in the diagnosis of reduced blood flow. Method: Patients with both diseases diabetic and hypertension were classified according to their age to identify the progression of the disease and factors influencing the carotid artery blood flow. By using ultrasound and standard Doppler techniq
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Gestational diabetes mellitus is glucose intolerance of varying degree with onset or first detection duringpregnancy,it can causelong and short term morbidities in both the mother and the child, such as shoulder dystocia,preeclampsia, and high blood pressure. The most powerful endogenous vasoconstrictor peptide, urotensin II, andits receptor are involved in the etiology of gestational diabetes mellitus.Aim of the study: The study’s goal was to see if there is a link between Urotensin II levels and insulin resistancein pregnant women with gestational diabetes.Patients and method: A case-control study that was conducted in obstetrics and gynecology department atBaghdad Teaching hospital from the first of January 2019 to the end of D
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The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv
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Background: Double diabetes is the term used to describe situations in which a patient exhibits characteristics that are a combination of type 1 and type 2 Diabetes Mellitus. Metalloendopeptidase or Neprilysin is membrane-bound metallopeptidase. It has a wide range of physiological function and a variety of substrates. It has a significant impact on the proteolytic functions of the kidney, cardiovascular health, immunological response, cell proliferation, and fetal development. It also has a preventative effect on the onset of type 2 diabetes, obesity, and cancer. Objective: The study aims to assess the level of MEP in patients wi |
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Background: Background: Diabetes mellitus is a life-threatening disease. Global prevalence of diabetes mellitus is increasing rapidly providing a worrying indication and major threat to global health unless interventions are created through community awareness and knowledge regarding different aspect of DM.
Aims: To assess the level of awareness regarding diabetes risk factors, prevention and management among community members in Baqubah city and to identify any association between awareness level and some variables.
Methods: Across sectional study was carried out from the 1st of January - 30th of November 2019 in all primary health care centers (six centers) in center of Baqubah city. A convenien
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