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Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence.

S Manimurugan1, Saad Almutairi1, Majed Mohammed Aborokbah1

  • 1Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia.

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|January 22, 2022
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Summary

This study introduces a two-stage model for heart disease prediction using Internet of Medical Things (IoMT) data. It combines sensor data analysis and echocardiogram image classification for accurate diagnosis.

Keywords:
Internet of Medical Thingscloudheart disease predictionhybrid Faster R-CNN with SE-ResNet-101hybrid linear discriminant analysis with modified ant lion optimizationmedical image

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • The Internet of Medical Things (IoMT) offers novel approaches for continuous patient monitoring and data collection.
  • Early and accurate diagnosis of heart disease is crucial for effective treatment and improved patient outcomes.
  • Integrating diverse data sources, including sensor readings and medical imaging, can enhance diagnostic accuracy.

Purpose of the Study:

  • To develop and validate a two-stage model for heart disease classification and prediction.
  • To leverage IoMT sensor data and echocardiogram images for enhanced diagnostic capabilities.
  • To compare the performance of hybrid machine learning models in medical data and image analysis.

Main Methods:

  • A two-stage approach was employed: Stage 1 involved classifying sensor data using a hybrid linear discriminant analysis with modified ant lion optimization (HLDA-MALO).
  • Stage 2 focused on classifying echocardiogram images using a hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model.
  • Classification results from both stages were consolidated and validated for heart disease prediction.

Main Results:

  • The HLDA-MALO method achieved high accuracy in sensor data classification (96.85% for normal, 98.31% for abnormal).
  • The hybrid Faster R-CNN model demonstrated superior performance in echocardiogram image classification, achieving 99.15% maximum accuracy, 98.06% precision, 98.95% recall, 96.32% specificity, and a 99.02% F-score.
  • The integrated approach showed significant potential for accurate heart disease prediction.

Conclusions:

  • The proposed two-stage IoMT-based model effectively predicts heart disease by integrating sensor data and echocardiogram analysis.
  • Hybrid machine learning techniques, HLDA-MALO and Faster R-CNN with SE-ResNeXt-101, show significant promise for medical data and image classification.
  • This research highlights the potential of advanced AI and IoMT in revolutionizing cardiovascular diagnostics.