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Related Experiment Video

Updated: Jun 26, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization

Antony Jaya Mabel Rani¹, Chinnapillai Srivenkateswaran², Gurunathan Vishnupriya³

¹Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.

Pacing and Clinical Electrophysiology : PACE

|May 16, 2024

Summary

This study introduces an optimized SpinalNet model using Jellyfish Search Flow Regime Optimization (JSFRO) for accurate heart disease prediction. The approach effectively classifies cardiac conditions from large datasets, improving patient treatment outcomes.

Keywords:

ECG SpinalNet big data heart disease classification map reduce

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Area of Science:

Cardiology
Machine Learning
Big Data Analytics

Background:

Heart disease poses a significant mortality risk, necessitating accurate prediction for effective patient management.
Existing machine learning models struggle with large healthcare datasets for heart disease prediction.
Optimized models are crucial for handling big data in cardiac patient care.

Purpose of the Study:

To develop a big data approach for heart disease classification.
To implement an optimized SpinalNet model incorporating Jellyfish Search Flow Regime Optimization (JSFRO).

Main Methods:

Electrocardiogram (ECG) images are converted to binary format.
A MapReduce model is employed for feature extraction (statistical, shape, temporal) and classification.
SpinalNet is trained using JSFRO, a hybrid of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO).

Main Results:

The JSFRO-based SpinalNet achieved high performance metrics.
Achieved an accuracy of 90.8%.
Demonstrated sensitivity of 95.2% and specificity of 93.6%.

Conclusions:

The proposed JSFRO-based SpinalNet offers an effective solution for heart disease classification in big data environments.
This optimized model enhances the precision of cardiac condition prediction.
The methodology provides a robust framework for improving cardiac patient treatment through advanced machine learning.