problendoПроблемы ЭндокринологииProblems of Endocrinology0375-96602308-1430Endocrinology Research Centre10.14341/probl13459problendo-13459Research ArticleНарушения углеводного обменаCarbohidrates metabolism disturbanciesPredicting Anxiety in Individuals with Diabetes: A Comparative Analysis of Machine Learning AlgorithmsPredicting Anxiety in Individuals with Diabetes: A Comparative Analysis of Machine Learning Algorithmshttps://orcid.org/0000-0002-5772-5534BourkhimeH.BourkhimeH.

Hind Bourkhime, Medical Informatics Specialist, Medical Informatics and Data science Unit, Laboratory of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez;  Diagnostic center; Medical and Pharmaceutical Sciences and Translational Research, Laboratory of Epidemiology and Health Sciences Research, Faculty of Medicine and Pharmacy of Fez

B.P. 1893, Km 2.200 Route de Sidi Harazem, Fès 30070,

Hind Bourkhime, Medical Informatics Specialist

B.P. 1893, Km 2.200 Route de Sidi Harazem, Fès 30070

hbourkhime@gmail.comhttps://orcid.org/0000-0002-1786-5049QarmicheN.QarmicheN.

Noura Qarmiche, Associate Professor in Bioinformatics, Laboratory of Artificial Intelligence, Data Science and Emerging Systems, National School of Applied Sciences Fez; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez

Fez

Noura Qarmiche, Associate Professor in Bioinformatics

Fez

noura.qarmiche@usmba.ac.mahttps://orcid.org/0000-0003-1011-504XBenmaamaS.BenmaamarS.

Soumaya Benmaamar, Associate Professor in Epidemiology, Diagnostic center; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez

Fez

Soumaya Benmaamar, Associate Professor in Epidemiology

Fez

soumaya.benmaamar@usmba.ac.maLazarN.LazarN.

Nada Lazar, Endocrinology Specialist, Diagnostic center; Department of Endocrinology, Diabetology, Metabolic Diseases and Nutrition

Fez

Nada Lazar, Endocrinology Specialist

Fez

nada.lazar@usmba.ac.maOmariM.OmariM.

Mohammed Omari, Medical Informatics Specialist, Medical Informatics and Data science Unit, Laboratory of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez; Diagnostic center

Fez

Mohammed Omari, Medical Informatics Specialist

Fez

mohammed.omari@usmba.ac.maBerrahoM.BerrahoM.

Mohamed Berraho, Full Professor of Epidemiology, Diagnostic center; Medical and Pharmaceutical Sciences and Translational Research, Laboratory of Epidemiology and Health Sciences Research, Faculty of Medicine and Pharmacy of Fez; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez

Fez

Mohamed Berraho, Full Professor of Epidemiology

Fez

maberraho@gmail.comTachfoutiN.TachfoutiN.

Mohamed Berraho, Full Professor of Epidemiology, Diagnostic center; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez

Fez

Nabil Tachfouti, Full Professor of Epidemiology

Fez

nabil.tachfouti@usmba.ac.maEL FakirSEL FakirS.

Samira EL Fakir, Full Professor of Epidemiology, Diagnostic center; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez

Fez

Samira EL Fakir, Full Professor of Epidemiology

Fez

samira.elfakir@usmba.ac.maEl OuahabiH.El OuahabiH.

Samira EL Fakir, Full Professor of Epidemiology, Diagnostic center; Department of Endocrinology, Diabetology, Metabolic Diseases and Nutrition

Fez

Hanan El Ouahabi, Full Professor of Endocrinology

Fez

hanan.elouahabi@usmba.ac.maOtmaniN.OtmaniN.

Nada Otmani, Full Professor of Medical Informatics, Medical Informatics and Data science Unit, Laboratory of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez; Diagnostic center

Fez

Nada Otmani, Full Professor of Medical Informatics

Fez

nada.otmani@usmba.ac.maSidi Mohamed Ben Abdellah University; Hassan II University Hospital; Sidi Mohamed Ben Abdellah UniversityМароккоMedical 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 UniversityMoroccoSidi Mohamed Ben Abdellah University; Sidi Mohamed Ben Abdellah UniversityМароккоLaboratory of Artificial Intelligence, Data Science and Emerging Systems, National School of Applied Sciences Fez, Sidi Mohamed Ben Abdellah University; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez, Sidi Mohamed Ben Abdellah UniversityMoroccoHassan II University Hospital; Sidi Mohamed Ben Abdellah UniversityМароккоDiagnostic center, Hassan II University Hospital; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez, Sidi Mohamed Ben Abdellah UniversityMoroccoHassan II University HospitalМароккоDiagnostic center, Hassan II University Hospital; Department of Endocrinology, Diabetology, Metabolic Diseases and Nutrition, Hassan II University HospitalMoroccoSidi Mohamed Ben Abdellah University; Hassan II University HospitalМароккоMedical 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 HospitalDiagnostic center, Hassan II University HospitalMoroccoHassan II University Hospital; Sidi Mohamed Ben Abdellah UniversityМароккоDiagnostic 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; Department of Epidemiology, Clinical Research and Community Health, Faculty of Medicine and Pharmacy of Fez, Sidi Mohamed Ben Abdellah UniversityMoroccoSidi Mohamed Ben Abdellah University; Hassan II University HospitalМароккоMedical 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 HospitalMorocco2026200520267225460Copyright © Bourkhime H., Qarmiche N., Benmaama S., Lazar N., Omari M., Berraho M., Tachfouti N., EL Fakir S., El Ouahabi H., Otmani N., 20262026Bourkhime H., Qarmiche N., Benmaama S., Lazar N., Omari M., Berraho M., Tachfouti N., EL Fakir S., El Ouahabi H., Otmani N.Bourkhime H., Qarmiche N., Benmaamar S., Lazar N., Omari M., Berraho M., Tachfouti N., EL Fakir S., El Ouahabi H., Otmani N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.probl-endojournals.ru/jour/article/view/13459

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.

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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The authors declare that there are no conflicts of interest present.