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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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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program

Tadesse M Abegaz¹, Muktar Ahmed², Fatimah Sherbeny¹

¹Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA.

Healthcare (Basel, Switzerland)

|April 28, 2023

Summary

This summary is machine-generated.

Machine learning models can predict uncontrolled diabetes. The random forest algorithm showed the highest accuracy, utilizing patient data like potassium levels and body weight.

Keywords:

All of Us Research Program machine learning prediction serum electrolytes uncontrolled diabetes

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Area of Science:

* Computational biology and bioinformatics
* Health informatics and predictive modeling

Background:

* A significant need exists for robust predictive models for uncontrolled diabetes mellitus.
* Existing models often lack the predictive power to identify individuals at high risk.

Purpose of the Study:

* To evaluate the efficacy of various machine learning algorithms in predicting uncontrolled diabetes mellitus.
* To identify key patient characteristics that serve as important predictors for uncontrolled diabetes.

Main Methods:

* Application of machine learning algorithms including Random Forest, Extreme Gradient Boost, Logistic Regression, and Weighted Ensemble.
* Utilized a dataset from the All of Us Research Program, including adult patients with diabetes.
* Included demographic data, biomarkers, and hematological indices as predictive features.

Main Results:

* The Random Forest model achieved the highest prediction accuracy (0.80) and Area Under the Curve (0.77).
* Key predictors identified include potassium levels, body weight, aspartate aminotransferase, height, and heart rate.
* Serum electrolytes and physical measurements emerged as crucial features for prediction.

Conclusions:

* Machine learning, particularly the Random Forest model, shows significant promise for predicting uncontrolled diabetes.
* Incorporating clinical characteristics like serum electrolytes and physical measurements enhances predictive capabilities.
* These findings support the development of data-driven tools for proactive diabetes management.