Advanced Cardiac Health Assessment: Integrating Deep Learning For Enhanced Clinical Decision Support In Heart Disease Prediction

Advanced Cardiac Health Assessment: Integrating Deep Learning For Enhanced Clinical Decision Support In Heart Disease Prediction

Registration ID: IJNRD_214629

Published ID: IJNRD2404169

DOI: Click Here to Get

S.Praveeshna , Dr.J.B.Shajilin Loret

heart disease,cnn,machine learning

The biggest health problem or obstacle that modern medicine faces worldwide is heart disease. It is now a major contributing element to the rising death rate. If heart illness is not detected early on, its severity is far more serious and may have dangerous repercussions. Techniques include electronic health records, ongoing body monitoring via a network, and patient health condition diagnosis through the use of wearable devices and medical sensor projections on human bodies. Since the human body generates enormous amounts of data on a continual basis, data mining techniques are used to efficiently classify the gathered health data. Furthermore, because it requires precise execution, the classification of health data is the most important procedure. First, a useful technique for feature selection and classification is used to forecast cardiac disease. The suggested study uses an unsupervised feature selection method and an optimized MLP-EBMDA (Multi-Layer Perceptron for Enhanced Brownian Motion-based Dragonfly Algorithm) for classification in heart disease prediction. The dataset will be used as the input for this implementation, and pre-processing will be done before the suggested feature selection technique—which effectively selects features—is used. The novel MLP-EBMDA is used in heart disease classification VI, helping to predict heart disease early on based on specific features. With an accuracy rating of 94.28 percent, the suggested technique may successfully predict heart illness as normal or abnormal

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"Advanced Cardiac Health Assessment: Integrating Deep Learning For Enhanced Clinical Decision Support In Heart Disease Prediction", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b525-b533, April-2024, Available :https://ijnrd.org/papers/IJNRD2404169.pdf

Volume 9 Issue 4, April-2024

Pages : b525-b533

Paper Reg. ID: IJNRD_214629

Published Paper Id: IJNRD2404169

Downloads: 000121209

Research Area: Information Technology 

Country: Tenkasi, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404169.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404169

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

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

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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