Temporal changes in temperature, humidity, height of atmospheric pressure and their impact on the psycho-physical functioning of man since the ancient Greeks have been the subject of study. At the beginning of the 21st century, scientists around the world are presenting irrefutable evidence that meteorological phenomena significantly contribute to the worsening of the condition in chronic patients, and also cause various health problems among healthy weather-sensitive people. Modern medicine defines a person whose organism is not able to adapt to changes in weather conditions as meteoropaths, and the state in which their orgasm is during that period is called meteorotropic.

Introduction Contraception is the most favourable method of family planning. Prevention of unwanted pregnancy has great significance, both as a measure of health preservation and as a wider preventive and social measure. Objective to evaluate how informed the adolescents are when it comes to contraception, as well as to evaluate their personal experiences. Methods the research was conducted in the form of a cross-sectional study. The research instrument was a questionnaire the adolescents were asked to fill, which contained questions constructed to assess how well they were informed about the importance of contraception as well as their personal experiences. The study included 120 students and the sample was intentional, as the study was conducted at the Faculty of Health and Business Studies in Valjevo, Singidunum University in April-May 2019. The participation in the study was voluntary and anonymous, and the research was approved by the authorities of the institution. Results the results were analysed using descriptive statistical methods. The respondents were between 19 and 23 years of age and predominantly female (83%), while only 17% were male. The majority of the respondents were well informed on the subject of mechanical contraception methods and the use of condoms. The condom was the most commonly used method of contraception during sexual intercourse (63%). A large number of respondents (40%) believed that adolescents could do with more information on the subject. Conclusion the research results indicate that there is a need for more education on the subject of the importance and methods of contraception. It is necessary to intensify health education in cooperation with health institutions, schools, higher education institutions and the entire community. This need is felt by the adolescents themselves, who believe that their peers do not know enough about contraception.

In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health “Dr. Milan Jovanovic Batut” in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia.

Hyperinsulinemia is a condition with extremely high levels of insulin in the blood. Various factors can lead to hyperinsulinemia in children and adolescents. Puberty is a period of significant change in children and adolescents. They do not have to have explicit symptoms for prediabetes, and certain health indicators may indicate a risk of developing this problem. The scientific study is designed as a cross-sectional study. In total, 674 children and adolescents of school age from 12 to 17 years old participated in the research. They received a recommendation from a pediatrician to do an OGTT (Oral Glucose Tolerance test) with insulinemia at a regular systematic examination. In addition to factor analysis, the study of the influence of individual factors was tested using RBF (Radial Basis Function) and SVM (Support Vector Machine) algorithm. The obtained results indicated statistically significant differences in the values of the monitored variables between the experimental and control groups. The obtained results showed that the number of adolescents at risk is increasing, and, in the presented research, it was 17.4%. Factor analysis and verification of the SVM algorithm changed the percentage of each risk factor. In addition, unlike previous research, three groups of children and adolescents at low, medium, and high risk were identified. The degree of risk can be of great diagnostic value for adopting corrective measures to prevent this problem and developing potential complications, primarily type 2 diabetes mellitus, cardiovascular disease, and other mass non-communicable diseases. The SVM algorithm is expected to determine the most accurate and reliable influence of risk factors. Using factor analysis and verification using the SVM algorithm, they significantly indicate an accurate, precise, and timely identification of children and adolescents at risk of hyperinsulinemia, which is of great importance for improving their health potential, and the health of society as a whole.

Introduction. Emergency or postcoital contraception is a method of contraception that is used within 72 hours after unprotected intercourse. It is very important that adolescents consider emergency contraception with awareness. It is not a regular method of contraception. The aim of this study was to examine the knowledge and attitudes of adolescents towards the use of emergency contraception. Material and Methods. The research was a cross-sectional study that used a specially designed questionnaire for adolescents intended to assess their knowledge about emergency contraception methods. The study included an intentional sample of 108 students attending the Medical High School ?Dr. Misa Pantic? in Valjevo. Participation in the study was voluntary and anonymous. Results. The largest number of students was informed about the indications for emergency contraception (80%, c2 = 0.004); the respondents agreed that emergency contraception is not a regular method of contraception and should be used in cases of sexual abuse (c2 = 0.019). Most of the respondents believe that they need additional education (N = 95, c2 = 0.032) regarding emergency contraception methods. Conclusion. The analysis of the results showed that the adolescents who participated in the study need additional education about the methods of emergency contraception. It is necessary to improve the strategies of health education of adolescents on this topic.