Prediction of MMSE Scores for Cognitive Impairment

Blekinge Institute of Technology693 enrolled

Overview

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.

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

693

Conditions

Cognitive Impairment Healthy Control

Interventions

MMSE ≤26OTHER

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

Eligibility

Sex: ALLMin age: 60 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

Blekinge institute of Technology

Karlskrona, Sweden

Outcomes

Primary Outcomes

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

Time frame: 5 mounths

Data from ClinicalTrials.gov

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