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Edge computing-based ensemble learning model for health care decision systems.

Asir Chandra Shinoo Robert Vincent1, Sudhakar Sengan2

  • 1Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627451, India. racshinoo2022@gmail.com.

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Summary
This summary is machine-generated.

This study introduces an Ensemble Extreme Learning Machine (EN-ELM) for clinical decision support systems (CDSS) to improve chronic illness diagnosis. The EN-ELM significantly enhances predictive accuracy, aiding healthcare professionals in better patient outcomes.

Keywords:
AccuracyAdaptive syntheticClinical decision support systemEdge computingExtreme learning machineMachine learning

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Artificial Intelligence in Healthcare

Background:

  • Rising prevalence of chronic illnesses necessitates accurate and rapid diagnostic and treatment procedures.
  • Traditional Machine Learning (ML) methods often exhibit limitations in reliable prediction for complex diseases.
  • Clinical Decision Support Systems (CDSS) aim to improve patient conditions and aid healthcare professionals' decision-making.

Purpose of the Study:

  • To propose an advanced Clinical Decision Support System (CDSS) utilizing an Ensemble Extreme Learning Machine (EN-ELM) algorithm.
  • To enhance the reliability and accuracy of diagnostic and medical treatment procedures for chronic illnesses.
  • To address challenges in traditional ML, such as overfitting, class imbalance, and outliers.

Main Methods:

  • Development of an Ensemble Extreme Learning Machine (EN-ELM) algorithm combining multiple trained predictors to mitigate overfitting.
  • Integration of data processing techniques like Adaptive Synthetic (ADASYN) and isolation Forest (iForest) to handle outliers and class imbalance.
  • Compatibility with an Edge Computing (EC) model for real-time computation and reduced system integration needs.

Main Results:

  • The proposed CDSS framework demonstrated significant improvements in classification performance across various medical datasets.
  • The EN-ELM model achieved high accuracy rates: 99.36% for Hepatocellular Carcinoma (HCC), 98.15% for Cervical Cancer, 97.85% for Chronic Kidney Disease (CKD), 97.06% for Heart Disease, and 96.72% for Arrhythmia.
  • An optimal ELM classification threshold of 85% was identified as most effective for boosting predictive model accuracy.

Conclusions:

  • The developed CDSS, powered by EN-ELM with ADASYN and iForest, offers a robust solution for accurate chronic disease diagnosis.
  • The system's high accuracy and real-time capabilities can significantly improve patient care and clinical decision-making.
  • Further validation across diverse medical datasets confirms the CDSS's potential to revolutionize chronic illness management.