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: 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.
Study Type
OBSERVATIONAL
Enrollment
2,000
A dataset comprising participants with T2D will be used to evaluate the classification performance of various machine-learning techniques.
Department of Health, Blekinge Institute of Technology
Karlskrona, Sweden
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
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