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Updated: Oct 17, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine

Deepak Kumar1, Chaman Verma2, Sanjay Dahiya3

  • 1Apex Institute of Technology, Chandigarh University, Mohali 140413, Punjab, India.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning models predict heart failure survival using key cardiac features. Elevated serum creatinine and serum sodium impact survival, guiding clinical focus for better patient outcomes.

Keywords:
IoTcardiac diseasefeature selectionmachine learningmulticollinearity

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Cardiovascular diseases and heart failure (HF) present a growing global health burden.
  • Effective forecasting of patient survival is crucial for managing HF.
  • Intelligent healthcare systems integrating machine learning and IoT offer promising solutions.

Purpose of the Study:

  • To develop a Public Key Infrastructure (PKI) secured IoT-enabled framework (CDF-DI) for cardiac disease feature and demographic identification.
  • To identify key features impacting heart failure patient survival, age group, and gender.
  • To enhance existing medical support systems for predicting heart patient survival.

Main Methods:

  • Analysis of a cardiac secondary dataset using statistical and machine learning techniques.
  • Application of Mann Whitney U test to assess the impact of Serum Creatinine (SC) and Serum Sodium (SS) on survival.
  • Utilizing Cox regression and Random Forest (RF) models to identify significant predictors.

Main Results:

  • Elevated SC and SS levels significantly impacted patient survival (p < 0.05).
  • Random Forest model achieved 96% accuracy in predicting survival using features: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets.
  • RF models also accurately predicted gender (94%) and age group (96%) based on specific cardiac features.

Conclusions:

  • Key features like follow-up months, SC, EF, CPK, and platelet count are vital for predicting heart patient survival.
  • The CDF-DI system and identified features can aid clinicians in forecasting survival status.
  • Focusing on these parameters can improve clinical decision-making for heart failure patients.