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Predicting the diabetes risk by analyzing symptoms using data mining techniques.

Rahaf Alhamouri1, Ahmad Alaiad1, Dania Rahhal1

  • 1Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan.

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Summary

This study demonstrates that Random Forest (RF) machine learning is highly effective for predicting diabetes risk. RF achieved over 97% accuracy, making it a valuable tool for early disease detection.

Keywords:
Diabetesalgorithmshealthmachine learningprediction

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Public Health

Background:

  • Diabetes mellitus is a prevalent chronic disease with significant individual and societal burdens.
  • Early prediction of diabetes risk is crucial for timely intervention and disease management.
  • Machine learning offers potential for developing predictive models for diabetes risk.

Purpose of the Study:

  • To evaluate the efficiency of various machine learning models in predicting diabetes risk.
  • To identify the most accurate algorithm for early diabetes risk assessment.
  • To explore the application of computational methods in diabetes prevention.

Main Methods:

  • Employed machine learning algorithms: Decision Tree, Naïve Bayes, Logistic Regression, and Random Forest (RF).
  • Utilized a dataset of 520 instances with 16 attributes related to diabetes risk symptoms.
  • Performance was assessed using accuracy, precision, recall, and F-measure, with 10-fold cross-validation and an 80:20 data split.

Main Results:

  • Random Forest (RF) significantly outperformed other algorithms, achieving 97.5% accuracy with 10-fold cross-validation.
  • RF demonstrated high performance metrics, including 95.2% accuracy using ratio splitting.
  • The model achieved a precision, recall, and F-measure of 0.975, indicating robust predictive capability.

Conclusions:

  • Random Forest (RF) is recommended as the optimal model for predicting diabetes risk, especially when combined with 10-fold cross-validation.
  • The study highlights the potential of machine learning in clinical decision-making for diabetes risk assessment.
  • Integrating predictive models like RF can enhance proactive healthcare strategies for diabetes prevention.