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Information on the geological structure of the Velykogadomynetske deposit of eluvial kaolins, zonation and material composition of the weathering crust is presented. Data on the mineral composition and physical and chemical characteristics of eluvial kaolins are presented. On the basis of the data of coordinates, description, results of well sampling drilled in different years by production geological organizations, chemical analysis of rocks and determination of their whiteness index, a target attributive electronic database was created (coordinates, description and data of well sampling, results of laboratory studies of rocks) on the basis of which structural (absolute elevations of the top and bottom of sediments, relief of the top and of the bottom and thickness of sediments) and material (material composition, content of iron and titanium oxides, whiteness) rock parameters of different weathering crustal zones and redeposited kaolins were studied. Cartographic constructions were made that visualize the structural (isohypses of the surface of the top and bottom, isopachites of the thickness of the disintegration and leaching zone, kaolinite-hydro-mica zone, pelicanite zone, alkaline kaolin zone, normal kaolin zone) and qualitative parameters (content of iron and titanium oxides and their lateral distribution) of the deposit rocks. The direction and strength of correlations between the surface of the top and the bottom of sediments and between their thickness, between the content of Fe₂O₃ and TiO₂ were studied and between the whiteness index and the content of iron and titanium oxides. It was found that the kaolin deposit has a complex occurrence and complex morphology, which are expressed in significant fluctuations in the absolute elevations of its top and bottom, changes in thickness, in the presence of substandard kaolin and are due to the geological and tectonic features of the deposit and the erosion section. The created structural-lithological model of the Velykogadomynetske deposit of eluvial kaolin reveals the cause-and-effect relationships between the geological and tectonic structure of the territory; structure, material composition, zonation and thickness of the weathering crust and its individual zones in particular; conditions of occurrence, material composition, thickness and spatial and vertical variability of qualitative indicators (whiteness indicator, content of harmful impurities of iron and titanium oxides) of the kaolin deposit.

Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients.