Prediction of Cardiac Arrest using Multiple Learning Algorithm

Q: What is the clinical evidence from this study?

Population: Cardiac arrest / Cardiovascular diseases. Intervention: Machine learning algorithms (Rough Set Theory, Artificial Neural Network, Random Forest, Support Vector Machine, and Modified Feature Elimination). Primary outcome: Early detection and prediction of cardiac arrest and cardiovascular diseases.

Q: What does this research mean for the field?

Machine learning and soft computing algorithms can provide rapid, early detection and prediction of cardiac arrest. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Niranjan Badu

doi:10.53365/nrfhh.2863

October 2, 2026Natural Resources for Human Health

Prediction of Cardiac Arrest using Multiple Learning Algorithm

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Key result

Machine learning models show promise for early cardiac arrest detection without reported quantitative outcomes.

Why the study?

Traditional methods for predicting cardiac arrest have not been effective because it often occurs without clear warning indicators, motivating the use of machine learning techniques for rapid, early detection of complex cardiac-related diseases.

Key Points

To establish rapid early detection and prediction models for cardiovascular diseases and sudden cardiac arrest associated with lifestyle risk factors.
Proposes an analytical framework utilizing soft computing and multiple machine learning models to handle complex cardiac disease data.
Incorporates Rough Set Theory (RST), Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM).
Applies Modified Feature Elimination (MREF) for feature selection and data preprocessing.
Presents an algorithmic strategy designed to overcome the limitations of traditional cardiac prediction methods that lack clear early warning indicators.
Outlines the integration of diverse lifestyle risk factors, including diet and smoking, to enable faster clinical detection and risk assessment.

PICO

P

Population

Cardiac arrest / Cardiovascular diseases

I

Intervention / Comparator

Machine learning algorithms (Rough Set Theory, Artificial Neural Network, Random Forest, Support Vector Machine, and Modified Feature Elimination)

O

Primary Outcome

Early detection and prediction of cardiac arrest and cardiovascular diseases

Implication

Proposes machine learning frameworks incorporating rough set theory and neural networks to predict cardiac arrest, highlighting avenues for early detection.

Abstract

Cardiac arrest (CA) is a leading cause of death worldwide and often occurs without warning. Early detection and intervention are critical to saving lives. Because there are no clear warning indicators, traditional methods for predicting CA have not been effective. When machine learning (ML) techniques are integrated, survival rates may increase since these techniques provide additional information that can lead to faster detection and treatment.Objective: This research proposes rapid, early detection methods for Cardiovascular Diseases (CVD). The reason behind CA is an irregular lifestyle, eating habits, smoking and several other factors. This study addresses the handling and prediction of complex cardiac-related diseases using machine learning.Method and Materials: This study plans to utilise the following machine learning and soft computing tools: Rough Set Theory (RST), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Modified Feature Elimination (MREF).

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Cite This Study

Niranjan Badu (2026) studied Cardiac arrest / Cardiovascular diseases. Machine learning algorithms (Rough Set Theory, Artificial Neural Network, Random Forest, Support Vector Machine, and Modified Feature Elimination) was evaluated on Early detection and prediction of cardiac arrest and cardiovascular diseases. Machine learning tools (RST, ANN, RF, SVM, and MREF) are proposed for early detection of cardiac arrest, though no quantitative results are reported in the abstract.

synapsesocial.com/papers/6ae28e56cbadf70daf6c41f1 https://doi.org/10.53365/nrfhh.2863

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Prediction of Cardiac Arrest using Multiple Learning Algorithm

Key Points

PICO

Implication

Abstract

Discussion

Cite This Study

Prediction of Cardiac Arrest using Multiple Learning Algorithm

Key Points

PICO

Implication

Abstract

Discussion

Cite This Study

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