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

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.

Study Overview

• Status: Completed
• Conditions: Cognitive Impairment; Healthy Control
• Intervention / Treatment: Other: MMSE ≤26

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 Type: Observational
• Enrollment (Actual): 693

Contacts and Locations

Study Locations

• Sweden
  • Karlskrona, Sweden, 37179
    • Blekinge Institute of Technology

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Data collected from the European collaborative study Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) and 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.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
MMSE ≤26; 339 participants	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.; Other Names: MMSE 30
MMSE 30; 354 participants	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.; Other Names: MMSE 30

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Detection perfomance	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	5 mounths

Collaborators and Investigators

• Sponsor: Blekinge Institute of Technology

Study record dates

Study Major Dates

• Study Start (Actual): June 10, 2024
• Primary Completion (Actual): October 10, 2024
• Study Completion (Actual): November 10, 2024

Study Registration Dates

• First Submitted: September 17, 2024
• First Submitted That Met QC Criteria: September 20, 2024
• First Posted (Actual): September 25, 2024

Study Record Updates

• Last Update Posted (Estimated): November 25, 2024
• Last Update Submitted That Met QC Criteria: November 22, 2024
• Last Verified: September 1, 2024

More Information

Terms related to this study

• Keywords: Cognitive Impairment; Oral Health; Classification; Machine Learning; Mini-Mental State Examination
• Additional Relevant MeSH Terms: Mental Disorders; Neurocognitive Disorders; Cognition Disorders; Cognitive Dysfunction
• Other Study ID Numbers: BTH-6.1.1-0156-2024

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Participant data can not be shared due to the GDPR.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No