Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data

Sergio E. Uribe; Jeļena Grišakova; Ilze Maldupa

doi:10.5281/zenodo.16367672

Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data

Sergio E. Uribe
, Jeļena Grišakova
, Ilze Maldupa

Rīga Stradiņš University

Research output: Guidelines, algorithms, methodologies & Other contribution › Other contribution › Research

Abstract

This research protocol outlines a method for studying early childhood caries risk factors in children aged 2-5 in Latvia.

Using a case-control design, it evaluates behavioral, dietary, and clinical determinants through parent interviews in various dental settings.

The study includes a sampling method with a sample size of 770, a validated 16-item questionnaire, and standardized interviewer training.

Key methods involve logistic regression modeling, factor analysis for risk clustering, and quality assurance measures to maintain data integrity.

The research aims to fill knowledge gaps about caries risk patterns specific to the population and offers evidence-based recommendations for pediatric dental practices in the Baltic region. It adheres to international research standards, including ethical principles from the Helsinki Declaration, transparent consent procedures, and strong data protection measures. The findings will aid in developing targeted preventive interventions and clinical tools for identifying high-risk children.

The project is funded by "RSU internal and RSU with LASE external consolidation" No. 5.2.1.1.i.0/2/24/I/CFLA/005.

Original language	English
Type	Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data
Publisher	Zenodo
Number of pages	14
Place of Publication	Rīga
DOIs	https://doi.org/10.5281/zenodo.16367672
Publication status	Published - 23 Jul 2025

Keywords*

Epidemiological studies
Machine learning
Dentistry
Early childhood caries
Dental Epidemiology
Latvia
Case-control study

Field of Science*

3.2 Clinical medicine
3.3 Health sciences

Publication Type*

6. Other publications

Access to Document

10.5281/zenodo.16367672Licence: CC BY-ND

EpiDentLatvia: Mapping the Epidemiological Profile of Oral Health in Latvian Children and Adolescents
E. Uribe, S. (Project leader) & Maldupa, I. (Expert)
Recovery and Resilience Facility
1/04/25 → 31/03/26
Project: Consolidation grants

Cite this

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title = "Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data",

abstract = "This research protocol outlines a method for studying early childhood caries risk factors in children aged 2-5 in Latvia. Using a case-control design, it evaluates behavioral, dietary, and clinical determinants through parent interviews in various dental settings. The study includes a sampling method with a sample size of 770, a validated 16-item questionnaire, and standardized interviewer training. Key methods involve logistic regression modeling, factor analysis for risk clustering, and quality assurance measures to maintain data integrity. The research aims to fill knowledge gaps about caries risk patterns specific to the population and offers evidence-based recommendations for pediatric dental practices in the Baltic region. It adheres to international research standards, including ethical principles from the Helsinki Declaration, transparent consent procedures, and strong data protection measures. The findings will aid in developing targeted preventive interventions and clinical tools for identifying high-risk children. The project is funded by {"}RSU internal and RSU with LASE external consolidation{"} No. 5.2.1.1.i.0/2/24/I/CFLA/005.",

keywords = "Epidemiological studies, Machine learning, Dentistry, Early childhood caries, Dental Epidemiology, Latvia, Case-control study",

author = "Uribe, \{Sergio E.\} and Jeļena Gri{\v s}akova and Ilze Maldupa",

year = "2025",

month = jul,

day = "23",

doi = "10.5281/zenodo.16367672",

language = "English",

publisher = "Zenodo",

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Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data. / Uribe, Sergio E.; Grišakova, Jeļena; Maldupa, Ilze.
14 p. Rīga: Zenodo. 2025, Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data.

Research output: Guidelines, algorithms, methodologies & Other contribution › Other contribution › Research

TY - GEN

T1 - Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data

AU - Uribe, Sergio E.

AU - Grišakova, Jeļena

AU - Maldupa, Ilze

PY - 2025/7/23

Y1 - 2025/7/23

N2 - This research protocol outlines a method for studying early childhood caries risk factors in children aged 2-5 in Latvia. Using a case-control design, it evaluates behavioral, dietary, and clinical determinants through parent interviews in various dental settings. The study includes a sampling method with a sample size of 770, a validated 16-item questionnaire, and standardized interviewer training. Key methods involve logistic regression modeling, factor analysis for risk clustering, and quality assurance measures to maintain data integrity. The research aims to fill knowledge gaps about caries risk patterns specific to the population and offers evidence-based recommendations for pediatric dental practices in the Baltic region. It adheres to international research standards, including ethical principles from the Helsinki Declaration, transparent consent procedures, and strong data protection measures. The findings will aid in developing targeted preventive interventions and clinical tools for identifying high-risk children. The project is funded by "RSU internal and RSU with LASE external consolidation" No. 5.2.1.1.i.0/2/24/I/CFLA/005.

AB - This research protocol outlines a method for studying early childhood caries risk factors in children aged 2-5 in Latvia. Using a case-control design, it evaluates behavioral, dietary, and clinical determinants through parent interviews in various dental settings. The study includes a sampling method with a sample size of 770, a validated 16-item questionnaire, and standardized interviewer training. Key methods involve logistic regression modeling, factor analysis for risk clustering, and quality assurance measures to maintain data integrity. The research aims to fill knowledge gaps about caries risk patterns specific to the population and offers evidence-based recommendations for pediatric dental practices in the Baltic region. It adheres to international research standards, including ethical principles from the Helsinki Declaration, transparent consent procedures, and strong data protection measures. The findings will aid in developing targeted preventive interventions and clinical tools for identifying high-risk children. The project is funded by "RSU internal and RSU with LASE external consolidation" No. 5.2.1.1.i.0/2/24/I/CFLA/005.

KW - Epidemiological studies

KW - Machine learning

KW - Dentistry

KW - Early childhood caries

KW - Dental Epidemiology

KW - Latvia

KW - Case-control study

U2 - 10.5281/zenodo.16367672

DO - 10.5281/zenodo.16367672

M3 - Other contribution

PB - Zenodo

CY - Rīga

ER -

Research Protocol - EpiDentLatvia - Usefulness of Machine Learning to identify Caries Risk Factors in Epidemiological Data

Abstract

Keywords*

Field of Science*

Publication Type*

Access to Document

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Projects

EpiDentLatvia: Mapping the Epidemiological Profile of Oral Health in Latvian Children and Adolescents

Cite this