NCT06611475

Prediction of MMSE Scores for Cognitive Impairment

Prediction of MMSE Scores for Cognitive Impairment: A Machine Learning Analysis of Oral Health and Demographic Data in Individuals Over 60 Years of Age

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 693 (actual)
• Sponsor: Blekinge Institute of Technology · Academic / Other
• Sex: All
• Age: 60 Years
• Healthy volunteers: Accepted

Summary

This study aims to explore the potential of using machine learning (ML) algorithms to predict cognitive status, specifically MMSE scores, 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 MMSE scores of 30 (normal cognition) or ≤26 (cognitive impairment) in individuals aged 60 and above.

Detailed description

This cross-sectional study utilizes oral health and demographic data from two existing cohort studies: the European collaborative study Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) and the Swedish National Study on Aging and Care (SNAC-B). Participants aged 60 years or older will be included in the analysis. The data will be used to develop and evaluate machine learning models for predicting cognitive status. Objectives: 1. Primary Objective: To assess the potential of oral health parameters for binary classification of MMSE scores (30 vs. ≤26). 2. Secondary Objective: To identify the most influential oral health parameters contributing to cognitive impairment predictions. 3. Tertiary Objective: To compare the performance of Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB) classifiers in predicting MMSE scores using oral health data.

Conditions

• Cognitive Impairment
• Healthy Control

Interventions

Type	Name	Description
OTHER	MMSE ≤26	A dataset comprising participants with MMSE scores of ≤26 and 30 will be used to evaluate the classification performance of various machine learning techniques.

Timeline

• Start date: 2024-06-10
• Primary completion: 2024-10-10
• Completion: 2024-11-10
• First posted: 2024-09-25
• Last updated: 2024-11-25

Locations

1 site across 1 country: Sweden