Type 2 DiabetesJFGNCT06981286
Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning
An observational study of Type 2 Diabetes, run by Blekinge Institute of Technology at 1 site in Sweden.
Trial 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… Read more about this study
- Who is it for?
- Ages 60 years and olderHealthy volunteers can take part
- Conditions the study is for
- Type 2 Diabetes
- What is being tested?
- A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.
- How many people
- 2,000 participants
- Where
- 1 siteSweden
- Study dates
- 30 Aug 2025to Jul 2027
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 60 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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
Who may be able to join
- Individuals aged 60 years or older.
- Participants with recorded oral health parameters with or without Diabetes type2
Who cannot join
- Individuals with Diabetes type1
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Sweden1 site
Department of Health, Blekinge Institute of Technology
Karlskrona, 37179
Johan Flyborg, 0707283117, johan.flyborg@bth.se
johan.flyborg@bth.se
About this study
Official title: 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.
Read the 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:
- 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.
- Conditions
- Type 2 Diabetes
- Being tested
- A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.
- Keywords
- Oral Health, Machine Learning, Diabetes Type 2
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| T2D | Older individuals with Diabetes type 2 Receives: 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 | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 May 2025
- Study start30 Aug 2025 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Type 2 DiabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Blekinge Institute of Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06981286
- Sponsor's protocol ID
- DT2 prediction
- Acronym
- JFG
- Status last verified
- May 2025
- First submitted
- 7 Apr 2025
- Last updated
- 20 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Participant data can not be shared due to the GDPR.
Study at a glance
From the registry record, last updated by the sponsor on 20 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning in Older Individuals With Mild Cognitive Impairment
- ClinicalTrials.gov ID
- NCT06981286
- Acronym / sponsor ID
- JFG / DT2 prediction
- Conditions
- Type 2 Diabetes
- Keywords
- Oral Health, Machine Learning, Diabetes Type 2
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Blekinge Institute of TechnologyUniversity, hospital or other
- Study start
- 30 Aug 2025 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Jul 2027 (estimated)
- First posted
- 20 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| T2D | – | Other: A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.Older individuals with Diabetes type 2 |
| Group/Cohort Description: Older individuals without Diabetes type 2 | – | – |
Who may qualify
- Ages
- 60 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (2 listed)
- Individuals aged 60 years or older.
- Participants with recorded oral health parameters with or without Diabetes type2
Key exclusion criteria (1 listed)
- Individuals with Diabetes type1
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Other | T2D |
Primary outcome
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 May 2025.
- Individual participant dataSharing planned: No. Participant data can not be shared due to the GDPR.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Type 2 Diabetes, run by Blekinge Institute of Technology at 1 site in Sweden.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 20 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Johan Flyborg, DDS, PhD (+46707283117, johan.flyborg@bth.se), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection perfomance (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 May 2025.