NCT06981286

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

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

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 2,000 (estimated)
• Sponsor: Blekinge Institute of Technology · Academic / Other
• Sex: All
• Age: 60 Years
• Healthy volunteers: Accepted

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.

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.

Conditions

• Type 2 Diabetes

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2025-08-30
• Primary completion: 2026-12-01
• Completion: 2027-07-01
• First posted: 2025-05-20
• Last updated: 2025-05-20

Locations

1 site across 1 country: Sweden