Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning in Older Individuals With Mild Cognitive Impairment
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 2,000
- 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 Diabetes type2, 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 type 2 diabetes in individuals with mild cognitive impairment aged 60 and above.
详细描述
This cross-sectional study utilizes oral health and demographic data from the Swedish National Study on Aging and Care (SNAC-B). Participants aged 60 years or older with Mild Cognitive Impairment will be included in the analysis. The data will be used to develop and evaluate machine learning models for predicting type 2 diabetes.
Objectives:
- Primary Objective: To assess the potential of oral health parameters for binary classification of type 2 diabetes or not.
- Secondary Objective: To identify the most influential oral health parameters contributing to type 2 diabetes predictions.
- Tertiary Objective: To compare the performance of Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB) classifiers in predicting type 2 diabetes 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 with or without Diabetes type2
排除标准
- •Individuals with Diabetes type1
结局指标
主要结局
Detection perfomance
时间窗: 12 months
Description: The study measures the classification performance of Machine Learning classifier. 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