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.
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Back ground: Diabetic nephropathy is rapidly becoming the leading cause of end-stage renal disease (ESRD). The onset and course of DN can be ameliorated to a very significant degree if intervention institutes at a point very early in the course of the development of this complication.
Objective: The aim of this study was to characterize risk factors associated with nephropathy in type I diabetes and construct a module for early prediction of diabetic nephropathy (DN) by analyzing their risk factors.
Methods: Case control design of 400 patients with type I diabetes mellitus (IDDM), aged 19-45 years. The cases were 200 diabetic patients with overt protein urea while the controls were 200 diabetic patients with no protein urea or micr
The clinical impact of interaction between body iron status (serum iron and ferritin) and type 2 diabetes has been investigated in this study. Thirty-six females were enrolled, eighteen type 2 diabetes and eighteen apparently healthy. These two groups were matched for age and body mass index BMI. The eighteen diabetes females were matched for age, BMI, pharmacological treatment (oral hypoglycemic agent), and chronic diabetes complications. The biochemical parameters measured for both groups (control and diabetes patient) were fasting insulin (Io), fasting blood glucose (Go), serum iron and ferritin. A significant increase in all parameters in patients compared to healthy control was noticed. The insulin resistance (IR) which was calculat
... Show MoreAutism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. The commonly used tools for analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A preliminary study on eye tracking trajectories of patients studied, showed a rudimentary statistical analysis (principal component analysis) provides interesting results on the statistical parameters that are studied such as the time spent in a region of interest. Another study, involving tools from Euclidean geometry and non-Euclidean, the trajectory of eye patients also showed interesting results. In this
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Objective: To investigate the relation between dyslipidemia and insulin resistance where it is one of the metabolic
disorders in patients with type-ΙΙ diabetes mellitus and compare the results with the control group.
Methodology: Blood samples were collected from (35) patients with type-ΙΙ diabetes mellitus, besides (35) healthy
individuals as a control group were enrolled in this study. The age of all subjects range from (20-50). Serum was
used in determination of glucose, insulin, lipid profile (cholesterol (Ch), triglyceride (TG), high-density lipoprotein
(HDL-Ch), low-density lipoprotein (LDL-Ch) and very low-density lipoprotein (VLDL), for patients and control
groups. Insulin resistance (IR) was calculated acco
The cost management of cost indicators in housing projects, on the level of planning and design, is the most important quality indicators, for adoption of strategies of planning and design efficient in managing these indicators. So this research points out the need to highlight the most effective and influential cost indicators in housing projects, and to determine strategies in the management of these indicators in order to raise the efficiency of housing projects quality, to seemly the income level target group, taking into consideration the quality of housing standards, to achieve the basic requirements of housing. This paper highlights the importance of the cost management, the types of housing cost, the method
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Gestational Diabetes Mellitus is known as carbohydrate intolerance first detected during pregnancy. Pregnancy is periods of intense hormonal changes. The aim of the present study was to investigate a possible relation between the changes in serum hormones such as Luteinizing hormone (LH) , follicle stimulating hormone(FSH), Progesterone, and Prolactin with gestational diabetes mellitus. Thirty patients with gestational diabetes mellitus aged (22 -40) year attending the national center for treatment and research of diabetes/ AL-Mustansiriya University in Baghdad and 29 controls aged (20-39) year were participated. Hormonal tests including, FSH, LH, Progesterone, and Prolactin were detected by using Enzyme Linked Fluorescent Assay (ELFA) k
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Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.
Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are
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