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Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Zahid Ullah1, Farrukh Saleem1, Mona Jamjoom2

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Computational Intelligence and Neuroscience
|October 10, 2022
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Summary
This summary is machine-generated.

Machine learning models effectively detect diabetes risk factors for early diagnosis. The k-nearest neighbor (KNN) model achieved 98.38% accuracy, outperforming other methods using the SMOTE-ENN technique for data balancing.

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Public Health

Background:

  • Diabetes mellitus is a chronic disease with severe complications, including cardiovascular, renal, and neurological damage.
  • Early identification of risk factors such as obesity, age, insulin resistance, and hypertension is crucial for disease management and prevention.
  • Machine learning (ML) offers a promising approach for analyzing complex health data to detect disease risk factors and support clinical decisions.

Purpose of the Study:

  • To develop and evaluate machine learning models for detecting diabetes risk factors.
  • To create a decision support system for medical practitioners to aid in early diabetes diagnosis.
  • To assess the efficacy of the Synthetic Minority Over-sampling Technique integrated with Edited Nearest Neighbor (SMOTE-ENN) for dataset balancing in diabetes prediction.

Main Methods:

  • Utilized the Behavioral Risk Factor Surveillance System (BRFSS) dataset.
  • Applied various machine learning algorithms after data preprocessing, including the SMOTE-ENN technique for balancing the dataset.
  • Trained and evaluated prediction models using metrics such as accuracy, sensitivity, specificity, and ROC/AUC scores.

Main Results:

  • The k-nearest neighbor (KNN) model demonstrated superior performance, achieving 98.38% accuracy and 98% for sensitivity, specificity, and ROC/AUC.
  • The proposed ML models, particularly KNN, showed high reliability and outperformed existing state-of-the-art methods.
  • The SMOTE-ENN method proved effective in balancing the dataset, leading to more accurate prediction models compared to using SMOTE alone.

Conclusions:

  • Machine learning, especially KNN combined with SMOTE-ENN, is highly effective for early diabetes risk factor detection and diagnosis.
  • The developed models provide a reliable decision support tool for healthcare professionals.
  • Optimizing dataset balancing techniques like SMOTE-ENN is vital for enhancing the performance of ML models in medical applications.