An observational study in Type 2 Diabetes, sponsored by Blekinge Institute of Technology. Not yet recruiting at 1 site in Sweden. Open to participants aged 60 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-05-20.
Sponsored by Blekinge Institute of Technology · Observational
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.
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:
10,371 studies on the registry are indexed under Diabetes Mellitus, Type 2; 1,406 are open to participants now.
This study's planned enrollment of 2,000 is above the median of 300 across 1,741 observational studies indexed under Diabetes Mellitus, Type 2.
Browse Diabetes Mellitus, Type 2 studies →Blekinge Institute of Technology is the lead sponsor of 14 studies on the registry; 6 are open to participants now.
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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
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Exclusion Criteria:
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.
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
Time frame: 12 months
Plan to share: No — Participant data can not be shared due to the GDPR.
No publications or documents are linked to this record.
Start is estimated for Aug 2025; primary completion for Dec 2026, 16 months later.
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This study is not yet recruiting, as verified in May 2025. You cannot join it, but the record below documents what was studied.
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Blekinge Institute of Technology