Prediction of MMSE Scores for Cognitive Impairment: A Machine Learning Analysis of Oral Health and Demographic Data in Individuals Over 60 Years of Age
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 693
- 试验地点
- 1
- 主要终点
- Detection perfomance
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 60 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Individuals aged 60 years or older.
- •Participants with recorded oral health parameters and MMSE scores of either 30 or ≤26.
排除标准
- •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.
结局指标
主要结局
Detection perfomance
时间窗: 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
次要结局
未报告次要终点