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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Advanced Cardiac Health Assessment: Integrating Deep Learning For Enhanced Clinical Decision Support In Heart Disease Prediction |
| Authors Name: | S.Praveeshna , Dr.J.B.Shajilin Loret |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_214629
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| Published Paper Id: | IJNRD2404169 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | 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 |
| Keywords: | heart disease,cnn,machine learning |
| Cite Article: | "Advanced Cardiac Health Assessment: Integrating Deep Learning For Enhanced Clinical Decision Support In Heart Disease Prediction", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b525-b533, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404169.pdf |
| Downloads: | 00037 |
| 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 |
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Published Paper ID:IJNRD2404169 Registration ID: 214629 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: b525-b533 Country: Tenkasi, Tamilnadu, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404169 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404169 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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