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Diabetes risk prediction model based on community follow-up data using machine learning.

Liangjun Jiang1, Zhenhua Xia2, Ronghui Zhu3

  • 1College of Information and Communication Engineering, State Key Lab of Marine Resource Utilisation in South China Sea, Hainan University, Haikou, China.

Preventive Medicine Reports
|September 1, 2023
PubMed
Summary

Community follow-up data reveals key lifestyle indicators impacting diabetes risk. A new model accurately predicts diabetes risk, aiding early detection and prevention strategies for better health outcomes.

Keywords:
Community follow-upDiabetes risk prediction modelDisease predictionMachine learningType 2 diabetes

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Area of Science:

  • Endocrinology and Metabolism
  • Public Health
  • Data Science

Background:

  • Diabetes mellitus is a chronic metabolic disorder requiring ongoing management, often within community settings.
  • The precise relationship between community follow-up lifestyle indicators and diabetes risk remains incompletely understood.
  • Effective community-based diabetes management necessitates identifying key predictive factors for risk stratification.

Purpose of the Study:

  • To investigate the association between key life characteristic indicators from community follow-up data and diabetes risk.
  • To develop and validate a robust diabetes risk assessment model using machine learning techniques.
  • To enhance the clinical applicability of diabetes risk prediction through a user-friendly scoring system.

Main Methods:

  • Analysis of 252,176 diabetes patient follow-up records (2016-2023) from Haizhu District, Guangzhou.
  • Application of feature selection techniques to identify optimal indicators influencing diabetes risk.
  • Development of a diabetes risk assessment model utilizing a random forest classifier with parameter optimization.

Main Results:

  • The random forest model achieved a high accuracy of 91.24% and an AUC of 0.97.
  • A subsequent diabetes risk score card, tested on original data, demonstrated superior accuracy at 95.15%.
  • The model effectively identifies critical lifestyle indicators for diabetes risk assessment.

Conclusions:

  • Big data mining of community follow-up records enables reliable diabetes risk prediction and early warning.
  • The developed model and score card offer practical tools for community physicians and personalized self-monitoring via devices.
  • Implementation can significantly promote diabetes prevention, control strategies, and lifestyle modification.