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Predicting Anxiety in Individuals with Diabetes: A Comparative Analysis of Machine Learning Algorithms.

H Bourkhime¹, N Qarmiche², S Benmaamar³

¹Medical Informatics and Data science Unit, Laboratory of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez, Sidi Mohamed Ben Abdellah University; Diagnostic center, Hassan II University Hospital; Medical and Pharmaceutical Sciences and Translational Research, Laboratory of Epidemiology and Health Sciences Research, Faculty of Medicine and Pharmacy of Fez, Sidi Mohamed Ben Abdellah University.

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|June 2, 2026

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Summary

Machine learning accurately predicts anxiety in diabetics. The Decision Tree model achieved 96% accuracy, offering a potential tool for improving care for diabetes patients with anxiety.

Area of Science:

Medical Informatics
Computational Psychiatry
Diabetes Management

Background:

Diabetes mellitus is a chronic condition associated with increased risk of anxiety disorders.
Effective management of diabetes and comorbid anxiety is crucial for patient well-being.
Predictive models can aid in early identification and intervention for anxiety in diabetic populations.

Purpose of the Study:

To compare the efficacy of three machine learning algorithms (Logistic Regression, Support Vector Machine, Decision Tree) for predicting anxiety in individuals with diabetes.
To evaluate the performance of these models using a Moroccan dataset.
To identify the most accurate algorithm for anxiety prediction in this cohort.

Main Methods:

Comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT) algorithms.

Related Experiment Videos

Utilized a Moroccan dataset comprising diabetic individuals.

Employed a grid search approach for hyperparameter tuning to optimize model performance.

Main Results:

The Decision Tree (DT) algorithm demonstrated the highest predictive accuracy at 96%.
Support Vector Machine (SVM) achieved an accuracy of 69%.
Logistic Regression (LR) yielded an accuracy of 61%.

Conclusions:

Machine learning algorithms show significant potential for predicting anxiety disorders in diabetic patients.
The Decision Tree model's high accuracy suggests its utility as a reliable tool in clinical settings.
Further research is needed to validate these findings and implement models in real-world healthcare scenarios to enhance patient care.