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

Diabetes is a long-term costly burden that increases the vulnerability of individuals to develop anxiety disorders. Consequently, effective management of diabetes anxiety in diabetics can significantly improve overall patient care.

This paper presents a comparative analysis of three machine learning algorithms, namely Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT), in predicting anxiety among diabetics. A Moroccan dataset was utilized, and a grid search approach was employed for hyperparameters tuning.

The findings demonstrate promising results in terms of the algorithms’ performance. The Decision Tree algorithm exhibited the highest accuracy, achieving an impressive 96% in predicting anxiety among diabetics. SVM followed with an accuracy rate of 69%, while LR achieved 61%. These outcomes provide valuable insights for further research endeavors aimed at refining the prediction models.

In conclusion, the study highlights the potential of machine learning algorithms in predicting anxiety disorders among individuals with diabetes. The high accuracy demonstrated by the Decision Tree model suggests its potential as a reliable tool in clinical settings. Further investigations are warranted to validate these results and explore the applicability of these models in real-world scenarios, ultimately enhancing the management and well-being of individuals with diabetes and comorbid anxiety disorders.

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