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 research Was based to on a real problem and realistically of represented by that Iraqi Airways company does not have the electronic cost accounting system and therefore be the process of the pricing various services provided by a company sample research respecting air transport and air cargo and aviation fuel and services and catering are not properly especially in the presence of new data from the new companies entering competition in Iraqi aviation industry and therefore does not provide price flexibility in order to compete in getting market share, And then research this problem addressed through design an electronic cost Accounting system covers all the costs incurred by the compan
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Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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The prevalence of diabetes is increasing rapidly and is now recognized as a significant global health problem. Diabetes occurs when a person does not produce enough insulin due to an imbalance in insulin production. This can lead to the failure of organs and tissues such as the kidneys, heart, blood vessels, eyes, nerves, and kidneys. As a result, early diagnosis and classification of type 2 diabetes mellitus (T2DM) are critical to aiding physician assessments. Subsequently, the current study aims to determine irisin levels in patients with T2DM and pre-DM as early predictors for disease cases. The current study included 138 subjects divided into three groups based on fasting blood glucose (FBG) and glycosylated hemoglobi
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Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered
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The financial markets are one of the sectors whose data is characterized by continuous movement in most of the times and it is constantly changing, so it is difficult to predict its trends , and this leads to the need of methods , means and techniques for making decisions, and that pushes investors and analysts in the financial markets to use various and different methods in order to reach at predicting the movement of the direction of the financial markets. In order to reach the goal of making decisions in different investments, where the algorithm of the support vector machine and the CART regression tree algorithm are used to classify the stock data in order to determine
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Background: Various studies conducted in many parts of the world suggest that there is lack of public awareness and knowledge of various aspects related to diabetes. With proper education, awareness, earlier detection and better care, many complications and co-morbidities can be reduced in diabetic population.Objectives: to evaluate the level of awareness of diabetes mellitus type 2 patients regarding their disease and its' complications.Methods: Cross – sectional survey was conducted during November and December 2011, in the Medical centers of Al Baladiat, Mustansyria and Zuafranya, including 145 type 2 diabetic patients (58.6 % males, 41.4% females) who were subjected to self–structured questionnaires regarding different aspects of
... Show MoreBACKGROUND: Diabetes Mellitus is a complex chronic illness that has increased significantly around the world and is expected to affect 628 million in 2045. Undiagnosed type 2 diabetes may affect 24% - 62% of the people with diabetes; while the prevalence of prediabetes is estimated to be 470 million cases by 2030. AIM OF STUDY: To find the percentage of undiagnosed diabetes and prediabetes in a slice of people aged ≥ 45years, and relate it with age, gender, central obesity, hypertension, and family history of diabetes. METHODS: A cross sectional study that included 712 healthy individuals living in Baghdad who accepted to take part in this study and fulfilling the inclusion and exclusion criteria.
... Show MoreBackground: Although there is evidence of peer support in high-income countries, the use of peer support as an intervention for cardiometabolic disease management, including type 2 diabetes (T2DM), in low- and middle-income countries (LMICs), is unclear. Methods: A scoping review methodology was used to search the databases MEDLINE, Embase, Emcare, PsycINFO, LILACS, CDSR, and CENTRAL. Results: Twenty-eight studies were included in this scoping review. Of these, 67% were developed in Asia, 22% in Africa, and 11% in the Americas. The definition of peer support varied; however, peer support offered a social and emotional dimension to help individuals cope with negative emotions and barriers while promoting disease management. Conclusio
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In general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t
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