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

NCT06981286 | Not Yet Recruiting

• 1 other identifier: DT2 prediction
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Type 2 Diabetes

Keywords

Oral Health; Machine Learning; Diabetes Type 2

Outcome Measures

Primary Outcomes (1)

• Detection perfomance

  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

Study Arms (2)

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.

Group/Cohort Description: Older individuals without Diabetes type 2

Interventions

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.

T2D

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

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

You may not qualify if:

• Individuals with Diabetes type1

Sponsors & Collaborators

• Blekinge Institute of Technology: lead

Study Sites (1)

Department of Health, Blekinge Institute of Technology

Karlskrona, 37179, Sweden

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Central Study Contacts

Johan Flyborg, DDS, PhD

CONTACT

+46707283117; johan.flyborg@bth.se

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 7, 2025
• First Posted: May 20, 2025
• Study Start: August 30, 2025
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): July 1, 2027
• Last Updated: May 20, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

SE (1)