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A Machine Learning Approach to Predicting Diabetes Complications.

Yazan Jian1, Michel Pasquier1, Assim Sagahyroon1

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.

Healthcare (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

This study effectively predicted eight diabetes complications using machine learning models, achieving high accuracy. Feature selection demonstrated that fewer features can still yield adequate predictive classifiers for diabetes management.

Keywords:
diabetes complicationsdiabetes predictionsupervised learning

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Diabetes Research

Background:

  • Diabetes mellitus (DM) is a chronic, life-threatening disease with severe complications affecting multiple organs.
  • Common DM complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy.

Purpose of the Study:

  • To develop and compare supervised classification models for predicting eight distinct diabetes complications.
  • To evaluate the impact of feature selection on model performance for diabetes complication prediction.

Main Methods:

  • Utilized a dataset of 884 records with 79 features from the Rashid Center for Diabetes and Research (RCDR).
  • Applied data preprocessing for missing values and class imbalance, followed by feature selection (top 5 and 10 features).
  • Employed repeated stratified k-fold cross-validation (k=10, 10 repetitions) and evaluated models using accuracy and F1-score.

Main Results:

  • Achieved maximum accuracy of 97.8% and F1-score of 97.7% in predicting diabetes complications.
  • Demonstrated that models built with a reduced set of selected features can still provide adequate classification performance.
  • Specific record counts for training classifiers varied per complication, e.g., 428 for metabolic syndrome, 836 for dyslipidemia.

Conclusions:

  • Supervised classification algorithms are effective in predicting multiple diabetes complications.
  • Feature selection is a viable strategy to build accurate predictive models while potentially simplifying them.
  • This approach can aid in early detection and management of diabetes-related complications.