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NCT Number: NCT06981286

Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning

This study aims to explore the potential of using machine learning (ML) algorithms to predict Diabetes type2, based on oral health and demographic data. The objective is to evaluate the effectiveness of various ML models and identify the most relevant oral health indicators for predicting type 2 diabetes in individuals with mild cognitive impairment aged 60 and above.

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Key information

Conditions

Type 2 Diabetes Diabetes Mellitus Diabetes Mellitus, Type 2 Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

60 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

This cross-sectional study utilizes oral health and demographic data from the Swedish National Study on Aging and Care (SNAC-B). Participants aged 60 years or older with Mild Cognitive Impairment will be included in the analysis. The data will be used to develop and evaluate machine learning models for predicting type 2 diabetes.

Objectives:

  • Primary Objective: To assess the potential of oral health parameters for binary classification of type 2 diabetes or not.
  • Secondary Objective: To identify the most influential oral health parameters contributing to type 2 diabetes predictions.
  • Tertiary Objective: To compare the performance of Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB) classifiers in predicting type 2 diabetes using oral health data.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Individuals aged 60 years or older.
  • Participants with recorded oral health parameters with or without Diabetes type2

Exclusion criteria

  • Individuals with Diabetes type1

Treatment and study plan

A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.

Other

A dataset comprising participants with T2D will be used to evaluate the classification performance of various machine-learning techniques.

Primary outcomes

  1. Detection perfomance

    Time frame: 12 months

    Description: The study measures the classification performance of Machine Learning classifier. Performance metrics, Accuracy, precision, recall, F1-Score and confusion matrix will be used for the evaluation. The examination of the most important features relied on SHAP summary plots, providing visualizations of the influence of parameter groups on the output, organized by their importance. This importance is based on SHAP values, offering insights into features' effects on the ML model's decision-making process

Study contacts

Contact information is provided by the study sponsor or research team.

Johan Flyborg, DDS, PhD

CONTACT

[email protected]

+46707283117

Sponsors and collaborators

Lead sponsor

Blekinge Institute of Technology

Other

Registry information

Official study title

Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning in Older Individuals With Mild Cognitive Impairment

Acronym: JFG

Important dates

Study start
2025
Primary completion
2026
Study completion
2027
First posted
May 20, 2025
Registry last updated
May 20, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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