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Not yet recruiting NCT06981286

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

— Observational Type 2 Diabetes

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques..
Who it may be relevant to
Registry conditions: Type 2 Diabetes. Basic parameters: from 60 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Sweden
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

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

Overview

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.

Interventions

  • 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.

Primary outcome measures

  • Detection perfomance [Time frame: 12 months]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Case-control

Study locations

Sweden · 1 center
  • Department of Health, Blekinge Institute of Technology — Karlskrona

Identifiers

NCT: NCT06981286 · DT2 prediction

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗