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Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine

Kirti Sharma1, Pawan K Tiwari1, S K Sinha1

  • 1Department of Physics, Birla Institute of Technology, Mesra, Ranchi 835215, India.

Journal of Chemical Information and Modeling
|February 5, 2025
PubMed
Summary
This summary is machine-generated.

This study predicts hematocrit volume using machine learning models based on glucose concentration. The XGBoost model showed promising accuracy for biomedical signal processing applications.

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Area of Science:

  • Biomedical Signal Processing
  • Machine Learning in Healthcare
  • Diabetes Management

Background:

  • Lifestyle diseases like diabetes significantly impact physiological metabolism and are linked to numerous other health conditions.
  • Effective health management for diabetes involves diet, exercise, and regular monitoring of blood glucose and hematocrit levels.
  • Current methods for monitoring hematocrit volume can be invasive or require specialized equipment.

Purpose of the Study:

  • To develop and evaluate machine learning models for estimating hematocrit volume from glucose concentration data.
  • To explore the efficacy of various regression models, including Linear Regression, Support Vector Regressor, Decision Tree, Random Forest Regressor, Artificial Neural Network, and Extreme Gradient Boosting Regressor.
  • To enhance diagnostic capabilities in biomedical signal processing by providing a non-invasive method for hematocrit estimation.

Main Methods:

  • Utilized amperometric signals from electrochemical glucose sensors to correlate glucose concentration with hematocrit volume.
  • Trained and tested multiple machine learning models (LR, SVR, DT, RFR, ANN, XGBoost) using an 80% training and 20% testing dataset in Python.
  • Evaluated model performance using R-squared, Mean Squared Error, and Root Mean Squared Error, with reliability assessed via relative error, K-fold cross-validation, and confidence interval analysis.

Main Results:

  • The Extreme Gradient Boosting (XGBoost) regression model demonstrated superior performance compared to Linear Regression and Artificial Neural Network models.
  • XGBoost achieved a 15% relative error between actual and predicted hematocrit values.
  • The XGBoost model exhibited 68% accuracy with a 6% standard deviation, validated through 5-fold cross-validation.

Conclusions:

  • Machine learning, particularly the XGBoost model, offers a viable approach for estimating hematocrit volume from glucose concentration data.
  • The XGBoost model's performance, flexibility, and interpretability make it suitable for predictive biomedical analytics.
  • This non-invasive method has the potential to improve diagnostic capabilities in managing conditions linked to diabetes and hematocrit levels.