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Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management

Huaiyan Jiang1, Han Wang1, Ting Pan1

  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China.

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Summary
This summary is machine-generated.

This study introduces a mobile app and machine learning for personalized diabetes management, using HbA1c levels to track progression. The intelligent system achieved 94.23% accuracy in predicting factors influencing glycated hemoglobin dynamics.

Keywords:
HbA1c dynamic predictiondeep learningdiabetes progression analysismachine learningmobile application

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Area of Science:

  • Digital health and machine learning applications in chronic disease management.
  • Biomarker analysis for metabolic disorder monitoring.

Background:

  • Diabetes mellitus affects over 500 million globally, requiring personalized management.
  • Glycated hemoglobin (HbA1c) is a key biomarker for monitoring long-term glycemic control and diabetes progression.

Purpose of the Study:

  • To develop an innovative approach for diabetes management by integrating a mobile application and machine learning.
  • To analyze the dynamics of HbA1c levels and identify influencing factors for personalized diabetes care.

Main Methods:

  • Creation of the DiabMini dataset with 127 features from 88 diabetic patients, including medical, personal, nutrient intake, and lifestyle data.
  • Development of a stacking machine learning model (XGBoost, SVC, ET, KNN) to predict HbA1c dynamics.
  • Application of SHapley Additive exPlanations (SHAP) for visualizing the impact of risk factors on HbA1c.

Main Results:

  • The developed stacking model achieved a classification accuracy of 94.23% in predicting HbA1c dynamics.
  • SHAP analysis effectively visualized and clarified the differential contributions of various risk factors to HbA1c levels.
  • The study successfully created a novel, comprehensive diabetes dataset (DiabMini).

Conclusions:

  • Integrating mobile health applications with machine learning offers a powerful tool for enhancing personalized diabetes management.
  • The intelligent system provides insights into factors affecting HbA1c dynamics, facilitating tailored interventions.
  • This approach holds significant potential for improving long-term diabetes control and patient outcomes.