Prediction of MMSE Scores for Cognitive Impairment

Completed

• Conditions: Cognitive Impairment; Healthy Control
• Interventions: Other: MMSE ≤26

• Registration Number: NCT06611475
• Lead Sponsor: Blekinge Institute of Technology

Brief 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.

Study Timeline

• Study Start: 2024-06-10
• Status Verified: 2024-09
• Study First Submitted: 2024-09-17
• Study First Submitted QC: 2024-09-20
• Study First Posted: 2024-09-25
• Primary Completion: 2024-10-10
• Study Completion: 2024-11-10
• Last Update Submitted: 2024-11-22
• Last Update Posted: 2024-11-25

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 693

Inclusion Criteria

• Individuals aged 60 years or older.
• Participants with recorded oral health parameters and MMSE scores of either 30 or ≤26.

Exclusion Criteria

• Individuals with MMSE scores of 27, 28, or 29, as these scores represent a transition phase between normal cognition and cognitive impairment, which could introduce variability.
• Individuals younger than 60 years.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
MMSE ≤26	MMSE ≤26	339 participants
MMSE 30	MMSE ≤26	354 participants

Primary Outcome Measures

Name	Time	Method
Detection perfomance	5 mounths	The study measures the classification performance of Machine Learning classifiers. 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

Trial Locations

Locations (1)

Blekinge institute of Technology; 🇸🇪 Karlskrona, Sweden