Evidence map›Paper›PMID 35628058›Full record

ArticleHealthcare (Basel, Switzerland)2022

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.

Igor Lukić, Nevena Ranković, Nikola Savić, Dragica Ranković, Željko Popov, Ana Vujić, Nevena Folić

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Igor LukićFaculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.
Nevena RankovićDepartment of Computer Science, School of Computing, Union University, 11000 Belgrade, Serbia.ORCID 0000-0002-9910-5886
Nikola SavićFaculty of Health and Business Studies, Singidunum University, 14000 Valjevo, Serbia.ORCID 0000-0002-6687-9455
Dragica RankovićDepartment of Mathematics and Statistics, Faculty of Applied Sciences in Nis, Union University "Nikola Tesla", 18000 Nis, Serbia.ORCID 0000-0002-4464-0726
Željko PopovSchool Center with Dormitory "Dositej Obradović", 24000 Subotica, Serbia.
Ana VujićFaculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.
Nevena FolićFaculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

children and adolescentsglucosehyperinsulinemiainsulinrisk factorsSVM

Identifiers

PMID35628058
PMCPMC9141748

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.