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.

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

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

Participant data can not be shared due to the GDPR.