AN PROACTIVE FEDERATED LEARNING MODEL USING CRNN FOR HEART DISEASE PREDICTION

Registration ID: IJNRD_226901

Published ID: IJNRD2408283

DOI: Click Here to Get

Dr.S.Balaji , Mr.Prabakar Rajasekaran , Dr.R.Saravanan

Federated Learning, Heart Disease Prediction, Convolutional Recurrent Neural Network (CRNN), Privacy-Preserving, Distributed Learning, Data Privacy, Healthcare

Federated learning(FL), a machine learning technique enabling collaborative model training on distributed devices or servers, has emerged as a powerful tool for healthcare applications. FL offers a significant advantage; it allows institutions to train powerful models on sensitive healthcare data while preserving patient privacy. FL also raises concerns about potential privacy leaks through vulnerabilities in the training process. Heart disease continues to be a significant global public health concern, necessitating the development of precise and effective predictive models to support early detection and treatment. The proposed system introduces a novel system for predicting heart disease using federated learning, a paradigm that enables cooperative model training across distributed data sources while maintaining data privacy. The architecture of the system is a convolutional recurrent neural network (CRNN), or CRNN for short. Medical records with inherent temporal structure are a good fit for CRNN analysis because of their superior sequential data processing capabilities. Recurrent neural networks (RNNs) are used to capture temporal dependencies in the data, whereas convolutional neural networks (CNNs) are used to extract features. To make more accurate predictions, the attention processes in the model enable it to concentrate on important aspects of the data. By employing federated learning with a CRNN architecture, The system aims to achieve a balance between model performance and data privacy in predicting heart disease risk. This approach has the potential to significantly improve early disease identification while safeguarding sensitive healthcare data.

Published Paper   E-Certificate

Dr.S.Balaji, Mr.Prabakar Rajasekaran, & Dr.R.Saravanan (August-2024). AN PROACTIVE FEDERATED LEARNING MODEL USING CRNN FOR HEART DISEASE PREDICTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), c757-c764. https://ijnrd.org/papers/IJNRD2408283.pdf

Volume 9 Issue 8, August-2024

Pages : c757-c764

Paper Reg. ID: IJNRD_226901

Published Paper Id: IJNRD2408283

Downloads: 000121994

Research Area: Science & Technology

Country: PONDICHERRY, PUDUCHERRY, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408283.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408283

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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