Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning
ConditionType 2 Diabetes
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age60+
SponsorBlekinge Institute of Technology
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Individuals aged 60 years or older.
Participants with recorded oral health parameters with or without Diabetes type2
Disqualifiers
None
Trial 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
Trial design
Design model
Case-control
Time perspective
Cross-sectional
Treatments tested in this trial
A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.
Other interventionA dataset comprising participants with T2D will be used to evaluate the classification performance of various machine-learning techniques.
Treatment groups
2,000 Participants
are divided into 2 treatment groupsGroup A: T2D1 intervention
Group B: Group/Cohort Description: Older individuals without Diabetes type 20 interventions
Trial outcomes
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
Time frame
12 monthsSponsors and contacts
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Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: