Prediction of MMSE Scores for Cognitive Impairment

NCT06611475 | Completed

• 1 other identifier: BTH-6.1.1-0156-2024
• Study Type: observational
• Target: 693
• Locations: 1 country
• Sites: 1

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.

Conditions

Cognitive Impairment; Healthy Control

Keywords

Classification; Machine Learning; Mini-Mental State Examination; Cognitive Impairment; Oral Health

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

MMSE ≤26

339 participants

Other: MMSE ≤26

MMSE 30

354 participants

Other: MMSE ≤26

Interventions

MMSE ≤26 OTHER

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

Also known as: MMSE 30

MMSE 30; MMSE ≤26

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Blekinge Institute of Technology: lead

Study Sites (1)

Blekinge institute of Technology

Karlskrona, 37179, Sweden

Location

MeSH Terms

Conditions

Cognitive Dysfunction

Condition Hierarchy (Ancestors)

Cognition Disorders; Neurocognitive Disorders; Mental Disorders

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 17, 2024
• First Posted: September 25, 2024
• Study Start: June 10, 2024
• Primary Completion: October 10, 2024
• Study Completion: November 10, 2024
• Last Updated: November 25, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will not share

Locations

SE (1)