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Data-Driven Machine-Learning Methods for Diabetes Risk Prediction.

Elias Dritsas1, Maria Trigka1

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Machine learning models can predict type 2 diabetes risk using common symptoms. Random Forest and K-NN showed the highest accuracy, aiding early diagnosis of this chronic condition.

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Endocrinology

Background:

  • Diabetes mellitus is a chronic metabolic disorder marked by hyperglycemia.
  • Increasing incidence necessitates early diagnosis and risk prediction tools.
  • Machine learning (ML) offers potential for developing efficient healthcare management solutions.

Purpose of the Study:

  • To develop high-efficiency risk prediction tools for type 2 diabetes occurrence using supervised learning.
  • To analyze feature importance and their association with diabetes development.
  • To evaluate and compare the performance of various ML models for diabetes risk prediction.

Main Methods:

  • A supervised learning methodology was employed.
  • Common diabetes symptoms were used as features to train and test ML models.
  • Model performance was evaluated using Precision, Recall, F-Measure, Accuracy, and AUC metrics under 10-fold cross-validation and data splitting.

Main Results:

  • Feature analysis identified key indicators associated with diabetes.
  • Random Forest and K-NN were identified as the best-performing ML models.
  • Both cross-validation and data splitting methods confirmed the superiority of Random Forest and K-NN.

Conclusions:

  • ML models, particularly Random Forest and K-NN, demonstrate high efficiency in predicting type 2 diabetes risk.
  • These models can serve as valuable tools for early diagnosis and management of diabetes.
  • Further development of ML-based tools can significantly improve diabetes care and patient outcomes.