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Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Nur Farahaina Idris1, Mohd Arfian Ismail1,2, Mohd Izham Mohd Jaya1

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This study introduces a novel stacking machine learning method for diabetes prediction, improving efficiency and accuracy. The Stacking Recursive Feature Elimination-Isolation Forest model effectively reduces complexity and outliers for better diabetes classification.

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Area of Science:

  • Computational biology and bioinformatics
  • Artificial intelligence in healthcare
  • Machine learning for disease prediction

Background:

  • Diabetes Mellitus is a chronic metabolic disorder with a growing global patient population, affecting all age groups.
  • Effective diabetes management is crucial to prevent severe health complications.
  • Integrating artificial intelligence (AI) enhances healthcare efficiency in diagnosis and patient care.

Purpose of the Study:

  • To investigate the potential of stacking ensembles in the diabetes domain.
  • To reduce the complexity and training time associated with stacking methods.
  • To improve diabetes classification performance by mitigating outliers in data.

Main Methods:

  • A novel machine learning approach, Stacking Recursive Feature Elimination-Isolation Forest, was developed for diabetes prediction.
  • Recursive Feature Elimination (RFE) was employed to create an efficient model using fewer features.
  • Isolation Forest was utilized as an outlier removal technique to enhance data quality.

Main Results:

  • The proposed method achieved an accuracy of 79.077% on the PIMA Indians Diabetes dataset.
  • An accuracy of 97.446% was obtained on the Diabetes Prediction dataset.
  • The method demonstrated superior performance compared to existing techniques in diabetes prediction.

Conclusions:

  • The Stacking Recursive Feature Elimination-Isolation Forest method is effective for diabetes prediction.
  • The approach successfully reduces model complexity and improves classification accuracy.
  • This AI-driven method offers a promising tool for efficient and accurate diabetes diagnosis.