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Related Experiment Video

Updated: Sep 28, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data.

M Thilagaraj1, B Dwarakanath2, V Pandimurugan3

  • 1Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, Coimbatore, India.

Computational and Mathematical Methods in Medicine
|March 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning algorithm (ERSEA) with a firefly-optimized LSTM model for advanced healthcare big data analytics. The proposed method significantly improves the accuracy, sensitivity, and specificity of medical data monitoring.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Data Science

Background:

  • The exponential growth of medical data from sensing devices necessitates advanced analytics for effective healthcare monitoring.
  • Traditional machine learning models face challenges in processing large-scale, real-time medical data streams.
  • There is a growing need for improved methods in healthcare big data analytics to extract insights and predict disease trends.

Purpose of the Study:

  • To propose a novel deep learning framework for efficient processing and analysis of healthcare big data.
  • To enhance medical data analytics and monitoring using a combination of electronic record search engine algorithm and optimized LSTM.
  • To address the challenges in applying learning models to big/medical data streams.

Main Methods:

  • Development of the electronic record search engine algorithm (ERSEA), a deep learning model.
  • Integration of a firefly optimized long short-term memory (LSTM) model for enhanced data analysis.
  • Experimentation using Apache Spark with diverse medical respiratory datasets.

Main Results:

  • The proposed ERSEA and firefly-optimized LSTM model achieved high performance metrics.
  • Accuracy, sensitivity, and specificity reached 94%, 93.5%, and 94% respectively for datasets < 5 GB.
  • For datasets > 5 GB, the model demonstrated 94% accuracy, 92% sensitivity, and 93% specificity.

Conclusions:

  • The proposed deep learning framework offers extraordinary performance in healthcare big data analytics and monitoring.
  • The ERSEA combined with firefly-optimized LSTM effectively handles large volumes of real-time medical data.
  • This approach represents a significant advancement in leveraging AI for medical data processing and disease prediction.