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A machine learning classifier-based approach for diabetes mellitus risk prediction.

Jai Kumar B1, Mohanasundaram Ranganathan2

  • 1SCHOOL OF COMPUTER SCIENCE AND ENGINEERING, Vellore Institute of Technology, VELLORE INSTITUTE OF TECHNOLOGY, VELLORE, Vellore, Tamil Nadu, 632014, INDIA.

Biomedical Physics & Engineering Express
|October 10, 2024
PubMed
Summary

This study enhances diabetes mellitus (DM) prediction using machine learning algorithms and feature engineering. Models like Extreme Gradient Boosting achieved high accuracy, precision, and recall, offering a promising tool for early diabetes risk forecasting.

Keywords:
Decision TreeDiabetes MellitusExtreme Gradient BoostingFeature EngineeringFeature ScalingMachine LearningType 2

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Data Science

Background:

  • Diabetes Mellitus (DM) poses significant health risks, often linked to lifestyle and dietary factors.
  • Elevated blood glucose and protein levels characterize diabetes, exacerbated by poor eating habits and sedentary lifestyles.
  • Obesity and related conditions are rising due to these lifestyle choices, increasing the urgency for effective prediction methods.

Purpose of the Study:

  • To investigate and compare various machine learning algorithms for diabetes mellitus risk forecasting.
  • To enhance the predictive accuracy of diabetes detection models through feature engineering and scaling.
  • To assess the efficacy of multiple classification techniques on a real-world diabetes dataset.

Main Methods:

  • Utilized eight distinct machine learning algorithms: Support Vector Classifier, Gradient Boosting, Multilayer Perceptron, Random Forest, K-Nearest Neighbors, Logistic Regression, Extreme Gradient Boosting, and Decision Tree.
  • Applied Feature Engineering (FE) and feature scaling techniques to optimize model performance.
  • Trained and evaluated models using the Mendeley diabetes dataset in Python.

Main Results:

  • Extreme Gradient Boosting and Decision Tree models demonstrated superior performance, achieving the highest F1 score (99.81%) and accuracy rate (99.80%).
  • High precision (99.81%) and recall (99.81%) were recorded for Extreme Gradient Boosting and Decision Tree, indicating robust predictive capabilities.
  • Other algorithms like Random Forest (99.21%) and Gradient Boosting (99.61%) also showed significant predictive accuracy.

Conclusions:

  • Machine learning, particularly Extreme Gradient Boosting and Decision Tree algorithms, offers highly accurate prediction of diabetes mellitus.
  • Feature engineering and scaling are crucial for improving the performance of diabetes prediction models.
  • The developed models provide a valuable tool for early risk assessment and management of diabetes.