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Related Experiment Video

Updated: Sep 21, 2025

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
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IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction.

Sasmita Padhy1, Sachikanta Dash2, Sidheswar Routray3

  • 1School of Computing Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India.

Computational Intelligence and Neuroscience
|May 31, 2022
PubMed
Summary

This study introduces an Internet of Things (IoT) and machine learning (ML) system for early diabetes prediction. The hybrid ensemble model effectively analyzes health data to improve diabetes management and patient monitoring.

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Diabetes Prediction

Background:

  • Diabetes affects 346 million globally, necessitating advanced monitoring and prediction tools.
  • Growing demand for Internet of Things (IoT)-based mobile healthcare applications for disease prediction.
  • Current healthcare systems require enhanced patient monitoring and technology-assisted decision-making for diabetes management.

Purpose of the Study:

  • To develop a noninvasive, IoT-based system using machine learning (ML) for early diabetes prediction.
  • To create enhanced diabetes management applications for improved patient monitoring.
  • To facilitate technology-assisted decision-making in diabetes care.

Main Methods:

  • A hybrid ensemble machine learning (ML) model combining bagging and boosting methods was developed.
  • Data collected from 10,221 participants via an online IoT application and an offline questionnaire.
  • Analysis of blood sugar and other key indicators for diabetes prediction.

Main Results:

  • The proposed hybrid ensemble ML model demonstrated superior performance in predicting diabetes mellitus.
  • Experimental findings indicate the model outperforms existing state-of-the-art techniques.
  • The system successfully integrated online (IoT) and offline data sources for comprehensive analysis.

Conclusions:

  • The developed IoT and ML-based system offers a promising approach for early diabetes prediction.
  • The enhanced diabetes management application aids in patient monitoring and informed decision-making.
  • This noninvasive self-care system has the potential to significantly improve diabetes management globally.