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A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases.

Wajid Shah1, Muhammad Aleem2, Muhammad Azhar Iqbal3

  • 1Capital University of Science and Technology, Islamabad 44000, Pakistan.

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This study introduces a machine learning system to predict vital signs for cardiovascular and chronic respiratory diseases. The Decision Tree model accurately classifies patient health status, enabling timely medical intervention.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Public Health Technology

Background:

  • Cardiovascular and chronic respiratory diseases cause millions of deaths globally each year.
  • Continuous monitoring of physiological parameters is crucial for managing these conditions.
  • Technological advancements can aid in early detection and intervention.

Purpose of the Study:

  • To develop a machine learning system for predicting future vital sign values.
  • To classify patient health status based on predicted vital signs for cardiovascular and chronic respiratory diseases.
  • To enable timely alerts for caregivers and medical experts.

Main Methods:

  • Utilized a real-world vital sign dataset for model training.
  • Employed regression techniques (linear, polynomial) to predict vital signs 1-3 minutes ahead.
  • Applied machine learning classifiers (SVM, Naive Bayes, Decision Tree) for health status classification.

Main Results:

  • The Decision Tree classifier demonstrated high accuracy in identifying abnormal vital sign values.
  • Short-term (60-second) and medium-term (3-minute) predictions were evaluated.
  • The system effectively assessed patient health status based on predicted vital signs.

Conclusions:

  • Machine learning-based vital sign prediction and classification can significantly aid patient management.
  • The Decision Tree model shows promise for real-time health status assessment.
  • This technology can facilitate timely medical care and improve outcomes for patients with chronic diseases.