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Diabetes Mellitus: Type 2 and Gestational

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...

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Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health

Zakhriya Alhassan^1,2, Matthew Watson¹, David Budgen¹

¹Department of Computer Science, Durham University, Durham, United Kingdom.

JMIR Medical Informatics

|May 24, 2021

Summary

Predicting elevated glycated hemoglobin (HbA_1c) is crucial for early diabetes detection. Machine learning models using patient history significantly improve prediction accuracy, enabling timely interventions for better health outcomes.

Keywords:

deep learning diabetes electronic health records glycated hemoglobin HbA1c longitudinal data machine learning multilayer perceptron neural network prediction time series data

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Area of Science:

Medical Informatics
Machine Learning in Healthcare
Predictive Analytics

Background:

Predicting glycated hemoglobin (HbA_1c) elevation aids in identifying individuals at risk for diabetes and related chronic conditions.
Early identification through advanced predictive models using electronic health records (EHR) can facilitate timely preventive interventions.
Utilizing EHR data for risk prediction can lead to improved patient health outcomes.

Purpose of the Study:

To evaluate the performance of various machine learning models in predicting HbA_1c elevation.
To assess the impact of using longitudinal EHR data on the predictive accuracy of these models.
To employ explainable AI methods for interpreting the decision-making processes of the predictive models.

Main Methods:

Employed logistic regression, random forest, support vector machine, and a deep learning (multilayer perceptron) model.
Classified patients into normal (<5.7%) and elevated (≥5.7%) HbA_1c categories.
Integrated current visit data with historical longitudinal data from 18,844 Saudi Arabian patient records.
Utilized explainable machine learning techniques for model interpretation.

Main Results:

Machine learning models demonstrated strong performance in predicting HbA_1c elevation risk.
Models incorporating longitudinal data significantly outperformed those using only current data and a standard logistic regression model.
The multilayer perceptron model achieved an 83.22% area under the ROC curve when utilizing historical data.
Key predictors like random blood sugar and age consistently influenced model predictions across all approaches.

Conclusions:

Machine learning models show significant promise for predicting current HbA_1c levels.
Incorporating longitudinal patient data enhances model performance and clarifies predictor importance.
The findings align with results from comparable studies, validating the utility of these predictive approaches.