Ignite Creation Date:
2024-10-26 @ 3:41 PM
Last Modification Date:
2024-10-26 @ 3:41 PM
Study NCT ID:
NCT06611475
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
None
First Post:
2024-09-17
Brief Title:
Prediction of MMSE Scores for Cognitive Impairment
Sponsor:
None
Organization:
None
Study Overview
Official 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
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None