A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

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

Det femte simsättet : En kvantitativ studie om undervattenkicksträning bland ungdomar

Syfte och frågeställningar Syftet med denna studie var att jämföra UVK-träning med ett generellt simträningsupplägg baserat på Simlinjen (Svenska  Simförbundet 2014) för svenska ungdomar. Studiens syfte var även att undersöka om en specifik UVK-träningsmetod leder till att simmarna implementerar UVK mer vid start och vändning samt om det påverkar farten respektive sluttiden under ett max lopp. Studiens frågeställning var: (1) Leder UVK-träningen till att simmarna kickar längre under vattnet i start och vändning under ett tävlingslopp? (2) Ökar UVK-farten efter start och vändning, bland ungdomar, efter en sexveckorsperiod av UVK-träning? Metod Totalt deltog 16 stycken försökspersoner, 8 pojkar och 8 flickor, i denna studie. En kvantitativ ansats har vidtagits för att studera skillnader på UVK-prestationen för svenska simungdomar efter ett sex veckor långt träningsprogram. Kontrollgruppen (KG) följde ett träningsupplägg baserat på Simlinjen (Svenska Simförbundet, 2014) medan UVK-gruppen följde ett mer UVK-baserat upplägg baserat på Leroy (2014). UVK-träningen bestod av vertikalkick, fotledsstyrka, ankelrörlighet och specifik UVK-träning. Innan och efter träningsperioden genomfördes två tester. Ett 15 meter maximal UVK-test och ett 50 meters ryggsimstest, för att mäta tid och fart av UVK samt bestämma sträcka av UVK vid ett maxlopp. Resultat Resultaten efter genomförd träningsperiod visar att UVK-gruppen kickar signifikant längre efter starten, samt visar en tendens till att kicka längre efter vändningen vid 50 meter ryggsim. KG visar en tendens till att vara snabbare till 10 meter vid 15 meter UVK. UVK-gruppen kickar signifikant längre än vad KG gör efter starten vid 50 meter ryggsim. Slutsats En period av specifik UVK-träning samt vertikalkicksträning leder till att ungdomar kickar längre efter starten, samt har en tendens till att kicka längre efter vändningen vid 50 meter ryggsim. Träningsmetoden kan implementeras som en del i ungdomars teknikutveckling för att kicka längre under vattenytan. Progression kan ske genom att först lära simmaren att kicka längre under vattnet, för att sedan lägga tid på att utveckla farten för att kicka denna sträcka snabbare

Internet Addiction among Secondary School Students Conditioned By Gender and Age

Modern forms of addiction are present very much in adolescents today. Internet dependency is a form of addiction that manifests itself as an individual's state of affairs. The use of the Internet has become the most important activity in life in relation to other everyday tasks and activities, to this extent and in that way, to isolate him from other social activities and to bring harmful consequences both to himself and to his family and the environment. Characteristic cases in adolescents that occur are insomnia, family disagreements, delays in school, or the absence and neglect of school obligations, nervousness, fatigue, physical changes such as neglecting personal hygiene, weight loss or other obesity, and etc. The number of Internet users and their addicts is growing every day. With this research, we want to determine whether it depends on gender and age and to what extent does it exist among high school students

Internet Addiction among Secondary School Students Conditioned By Gender and Age

Modern forms of addiction are present very much in adolescents today. Internet dependency is a form of addiction that manifests itself as an individual's state of affairs. The use of the Internet has become the most important activity in life in relation to other everyday tasks and activities, to this extent and in that way, to isolate him from other social activities and to bring harmful consequences both to himself and to his family and the environment. Characteristic cases in adolescents that occur are insomnia, family disagreements, delays in school, or the absence and neglect of school obligations, nervousness, fatigue, physical changes such as neglecting personal hygiene, weight loss or other obesity, and etc. The number of Internet users and their addicts is growing every day. With this research, we want to determine whether it depends on gender and age and to what extent does it exist among high school students

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children’s department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi’s orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual’s risk, the risk assessment of the entire monitored group is updated without having to analyze all data

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children’s department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi’s orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual’s risk, the risk assessment of the entire monitored group is updated without having to analyze all data

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children’s department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi’s orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual’s risk, the risk assessment of the entire monitored group is updated without having to analyze all data