Oral Health Parameter-Based Diabetes Type 2 Indication Using Machine Learning

Condition(s) studied

Investigational drug(s) / intervention(s)

A dataset comprising participants withT2D will be used to evaluate the classification performance of various machine learning techniques.: A dataset comprising participants with T2D will be used to evaluate the classification performance of various machine-learning techniques.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Detection perfomance — 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

Trial sites (1)

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