Prediction of MMSE Scores for Cognitive Impairment

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.

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Key information

Conditions

Cognitive Impairment Cognition Disorders Cognitive Dysfunction Healthy Control Mental Disorders Neurocognitive Disorders

Age range

60 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Blekinge institute of Technology

Karlskrona, 37179, Sweden

About this study

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:

Primary Objective: To assess the potential of oral health parameters for binary classification of MMSE scores (30 vs. ≤26).
Secondary Objective: To identify the most influential oral health parameters contributing to cognitive impairment predictions.
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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Other names: MMSE 30

Primary outcomes

Detection perfomance

Time frame: 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

Sponsors and collaborators

Lead sponsor

Blekinge Institute of Technology

Other

Registry information

Official study title

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

Important dates

Study start: 2024
Primary completion: 2024
Study completion: 2024
First posted: Sep 25, 2024
Registry last updated: Nov 25, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed