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Rationale, Aims and Objectives Method Results Conclusion This research aims to develop an effective algorithm for diagnosing COVID‐19 in chest X‐rays using the transfer learning method and support vector machines.In total, data was collected from 10 clinics, including both large city hospitals and smaller medical institutions. This ensured a diverse range of geographical and demographic information in the sample. An extensive data set was collected, including 10,000 chest X‐ray images. 5000 images represent normal cases, 3993 images represent pneumonia cases, and 1007 images represent COVID‐19 cases. Machine learning methods were applied to develop a classification model, and the results were compared with seven state‐of‐the‐art models and a lightweight CNN architecture.The results showed that the proposed method achieves high accuracy values (Accuracy): 0.95 for COVID‐19, 0.89 for pneumonia, and 0.92 for normal images (p < 0.05). Comparison with other models demonstrates statistically significant superiority of our method in accuracy across all three classes. The EfficientNet‐B0 model surpasses our method only in accuracy for normal images with p < 0.01, confirming the advantages of our method. Our method demonstrates high sensitivity values (Sensitivity): 0.96 for COVID‐19, 0.88 for pneumonia, and 0.93 for normal images (p < 0.05), outperforming most of the compared models. Correlation analysis showed Pearson coefficients of 0.92, 0.89, and 0.94 for COVID‐19, pneumonia, and normal images, respectively, confirming a high degree of consistency between predicted and true class labels. In addition, the model was validated on external datasets to assess its generalizability. This validation confirmed its high level of effectiveness in a variety of clinical settings.This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID‐19, as well as contribute to the development of innovative methods in medical practice. [ABSTRACT FROM AUTHOR]

According to the article, pregnancy in adolescence is associated with a high risk of developing adverse outcomes both during pregnancy and childbirth in the later period. The purpose of our study was to identify the features of pregnancy and childbirth, the postpartum period in young mothers. Materials and methods. A retrospective analysis of the birth history of 299 maternity hospitals was performed. The research material was archived data from the Regional perinatal center No. 3 in Turkestan (Kazakhstan). The main (1) group was formed by 199 maternity women under 19 years of age (2019). The control (2) group was formed by 100 maternity women aged 20 to 30 years, whose sexual life began after the age of 18. Results. The age of the surveyed women in group 1 ranges from 15 to 19 years, averaging ~16.9 years. 17-year-old girls predominated (66.7%). The average age of women in group 2 was ~25.8 years. Adolescent pregnancy is a risk factor for adverse child outcomes, such as premature birth, low birth weight, fetal growth retardation, neonatal and infant mortality. In the adolescent pregnancy and delivery group, preterm birth occurred in 35 cases, which accounted for 6.8% of the total preterm birth population for 2019, but in the adolescent birth group it was 17.5%. Of the 199 births in 2 were multiple births (1%), 197 live births, the percentage of live births among adolescents was 98%, respectively, the stillbirth rate was 2% (4 cases). Conclusion: the frequency of teenage pregnancy in the dynamics of years does not tend to decrease, among young mothers only every 6 received pre-pregnancy training, and every 5 was re-pregnant and among re-pregnant women under 19 years. [ABSTRACT FROM AUTHOR]