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.
In this paper, we build a fuzzy classification system for classifying the nutritional status of children under 5 years old in Iraq using the Mamdani method based on input variables such as weight and height to determine the nutritional status of the child. Also, Classifying the nutritional status faces a difficult challenge in the medical field due to uncertainty and ambiguity in the variables and attributes that determine the categories of nutritional status for children, which are relied upon in medical diagnosis to determine the types of malnutrition problems and identify the categories or groups suffering from malnutrition to determine the risks faced by each group or category of children. Malnutrition in children is one of the most
... Show More
(2)
Keys for 22 species representing ten genera Thripidae collection carried out during 1999-2001 in different localities in the middle of Iraq. Of them four species are described as new to science, Frankliniella megacephala sp. nov; Retithrips bagdadensis sp. nov; Chirothrips imperatus sp. nov; Taeniothrips tigridis sp. nov; Another thirteen species are recorded for the first time in Iraq; Thrips meridionalis (Pri.); Microcephalothrips abdominils (Crawford); Scolothrips pallidus (Beach); Scritothrips mangiferae Pri.; Frankliniella tritici Bagnall; Frankliniella schultzie Trybom; Frankliniella unicolor Morgan; Retithrips aegypticus Mar
... Show MoreThe purpose of this work is to study the classification and construction of (k,3)-arcs in the projective plane PG(2,7). We found that there are two (5,3)-arcs, four (6,3)-arcs, six (7,3)arcs, six (8,3)-arcs, seven (9,3)-arcs, six (10,3)-arcs and six (11,3)-arcs. All of these arcs are incomplete. The number of distinct (12,3)-arcs are six, two of them are complete. There are four distinct (13,3)-arcs, two of them are complete and one (14,3)-arc which is incomplete. There exists one complete (15,3)-arc.
(2)
(2)
These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t
... Show More
(49)
Citrus diseases are the main issue for agricultural productivity, which cause a reduction in yield and quality, thus causing economic loss; also, we see that traditional methods of diagnosis, which are very subjective and prone to error, and thus we need to suggest accurate automated detection systems. In this study, we present a hybrid model that integrates conventional machine learning with deep learning models. We used the Gray Level Co-occurrence Matrix (GLCM) for feature extraction, which is classified via machine learning algorithms like k-Nearest Neighbors (KNN), Decision Tree (DT), Linear Discriminant Analysis (LDA), and Random Forest (RF). At the same time, deep convolutional neural networks (DCNNs), GoogLeNet, and ResNet18
... Show More
Iraq within the ranks of the fledgling communities characterized by a broad base of the population pyramid, because they pose the age group (under 15 years) of a large proportion of the community, as it exceeded the proportion (40%) during the years of research extended (1986-2010) Despite the relative decline in the rates fertility during that period, but the proportion of young people remained high, especially for groups of at least five years, amounting to about 14% in 2012, a little more than the proportion of what constitutes age group (5-9 above) years, where it was (13%) and this naturally predicts continuing population increases in coming decades, due to the entry of those numbers of individuals in the reproductive stage,
... Show MoreGenetic variation was studied in 22 local and imported samples collected from local Iraqi market by using Single sequence repeat (SSR-PCR). Six primers set were used in this study. These primers produced 33 bands. Molecular weights of these bands ranged between 100 bp to 1500 bp. The number of polymorphic bands is 24, whereas the number of monomorphic bands is 9. The results of Dendrogram of the studied samples depended on SSR-PCR results by using Jaccard coefficient for genetic similarity was distributed the samples into 10 groups. This Dendrogram revealed a higher similarity between Iraqi/Balad green bell pepper and Iraqi/Yousifia green bell pepper with 1 value. This value is the highest between samples in comparison with lowest values (0
... Show MoreThe main purpose of the research is to demonstrate the importance of the insurance sector in the economy through its role in providing security for all economic sectors and thus stimulating the gross domestic product and reducing dependence on the output of the oil sector, which may expose the Iraqi economy to several problems and imbalances, I have found that there is a great weakness in the role of the insurance sector in Iraq at the level of government, companies and individuals, and the reason for this is the lack of policies supporting the insurance sector and the lack of work in the strategy of economic diversity and the decrease of security awareness by individuals so became Developing the sector to ensure the urgent need
... Show More