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

May 19, 2025 updated by: Blekinge Institute of Technology

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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:

  1. Primary Objective: To assess the potential of oral health parameters for binary classification of type 2 diabetes or not.
  2. Secondary Objective: To identify the most influential oral health parameters contributing to type 2 diabetes predictions.
  3. 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.

Study Type

Observational

Enrollment (Estimated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Sweden
      • Karlskrona, Sweden, 37179
        • Department of Health, Blekinge Institute of Technology
        • Contact:
        • Contact:
        • Principal Investigator:
          • Johan Flyborg, DDS,PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Data collected from the Swedish National Study on Aging and Care (SNAC-B) will be analyzed. Participants aged 60 years or older will be included in the analysis

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
T2D
Older individuals with Diabetes type 2
Other: A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.
A dataset comprising participants with T2D will be used to evaluate the classification performance of various machine-learning techniques.
Group/Cohort Description: Older individuals without Diabetes type 2

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Blekinge Institute of Technology

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 30, 2025

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

July 1, 2027

Study Registration Dates

First Submitted

April 7, 2025

First Submitted That Met QC Criteria

May 19, 2025

First Posted (Estimated)

May 20, 2025

Study Record Updates

Last Update Posted (Estimated)

May 20, 2025

Last Update Submitted That Met QC Criteria

May 19, 2025

Last Verified

May 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • DT2 prediction

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Participant data can not be shared due to the GDPR.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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