Insulin Resistance and β-Cell Function Prediction Without Fasting Insulin
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Solution Overview
Problem
Current clinical practices lack effective methods for predicting insulin resistance and pancreatic β-cell function in non-diabetic individuals, as static testing like HOMA-IR and HOMA-β are not routinely utilized due to the need for fasting plasma insulin levels, and existing machine learning models primarily focus on diabetes prediction rather than early indicators of insulin resistance and β-cell function.
Innovation Solution
A system and method utilizing a database, feature extraction, and machine learning model to predict insulin resistance and pancreatic β-cell function, incorporating features such as age, gender, and body mass index, with algorithms like XGboost, random forests, and deep neural networks for accurate prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HOMA-IR and HOMA-β testing is used to assess insulin resistance and β-cell function, then early diagnosis of diabetes can be achieved, but fasting plasma insulin level is required which is not routinely checked in clinical practice
Solution Approach 1:
The invention extracts the essential predictive information from the complex HOMA-IR and HOMA-β calculations by identifying and utilizing only the routinely available fasting plasma glucose parameter, eliminating the need for fasting plasma insulin measurement while preserving the core diagnostic capability through machine learning-based prediction models
Solution Approach 2:
The invention introduces machine learning prediction models as an intermediary that bridges the gap between routinely checked fasting plasma glucose and the clinically valuable but inaccessible insulin resistance and β-cell function metrics, allowing indirect assessment through computational prediction
2Measurement precision
If machine learning models are used for diabetes prediction, then prediction accuracy can be improved, but most studies focus on diabetes prediction rather than early indicators like insulin resistance and β-cell function in non-diabetic patients
Solution Approach 1:
The invention segments the diabetes prediction problem into distinct components by developing separate prediction models for insulin resistance and β-cell function that can be applied independently to non-diabetic populations, allowing early intervention before full diabetes develops
Solution Approach 2:
The invention performs preliminary prediction of insulin resistance and β-cell function in non-diabetic individuals before diabetes actually develops, enabling early identification of at-risk patients and timely intervention to prevent or delay disease onset
Data Source
AI summary
A system and a method for predicting insulin resistance and/or pancreatic β-cell function are provided, where a machine learning model is utilized to predict insulin resistance and/or pancreatic a decline of β-cell function of a subject in need thereof based on a feature set extracted from a database. Therefore, clinicians or the subject can be warned to take necessary actions on, and adjust related medical treatment or lifestyle before the subject is diagnosed with diabetes mellitus. In addition, a computer readable medium thereof is also provided.