Predictive Capability of a Condensed CNN Model in Discovering Patterns of Coronary Heart Disease

Registration ID: IJNRD_211938

Published ID: IJNRD2402316

: Click Here to Get

Darapureddy Manikanta Sai, uppalaNagamunesh, Darapureddy saimanikanta

A competent neural network with convolutional layers is reported in this article, ideal for recognizing extremely unexpected clinical data. The National Health and Nutritional Examination Survey (NHANES) is the source of the carefully collected data used to estimate rates of Coronary Heart Disease (CHD). When applied to this kind of data, typical machine learning algorithms usually have permanent class mismatch issues, even after class-specific weights have been modified. However, our updated two-layer CNN performs as well in each group, suggesting that it is resilient to such modifications. In the scenario of a very unequal dataset, we apply a two-pronged technique to acquire greater accuracy for both positive (actual CHD predictions) and negative (healthy cases) classes on an increasing test sample size. First, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to assess feature weights. This is replaced by a majority-voting mechanism for identifying significant qualities. A fully connected layer is a vital step prior to these essential qualities traveling across succeeding convolutional layers for comparison. To further increase overall classification accuracy, we provide an epoch-specific training strategy that replicates a simulated annealing process. Our recommended CNN architecture obtains an extraordinary classification accuracy of 77% for positive CHD cases and 81.8% for negative instances on the testing data, which represents for 85.70% of the overall dataset, despite the frequent class conflict in the NHANES dataset. This result implies the likely generalizability of the recommended strategy to other healthcare research with comparable feature ordering and discrepancies. Although our CNN model's memory scores are comparable to those of other machine learning approaches like SVM and random forest, our model is better at identifying negative (non-CHD) events. The recommended strategy may enhance medical treatments, minimize diagnostic charges, and develop study tools by putting a smart diagnostic system inside the healthcare framework. Notably, our model's total accuracy (79.5%) outperforms the accuracy of the SVM and random forest models separately. Data like as sensitivity, test accuracy, recall, and Area Under the Curve (AUC) are created using the CNN algorithm.

Published Paper   E-Certificate

Darapureddy Manikanta Sai, uppalaNagamunesh, & Darapureddy saimanikanta (February-2024). Predictive Capability of a Condensed CNN Model in Discovering Patterns of Coronary Heart Disease. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(2), d144-d157. https://ijnrd.org/papers/IJNRD2402316.pdf

Volume 9 Issue 2, February-2024

Pages : d144-d157

Paper Reg. ID: IJNRD_211938

Published Paper Id: IJNRD2402316

Downloads: 000122033

Research Area: Engineering

Author Type: Indian Author

Country: reaplle, andhrapradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402316.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2402316

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

UGC CARE JOURNAL PUBLICATION | 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 | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

© 2025 — Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License. and The Open Definition.

You are free to share, adapt, and redistribute the material, provided proper credit is given to the original author. 🛡️ Disclaimer: The content, data, and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use. The journal and its editorial board are not liable for any errors, losses, or consequences arising from its use.

Call For Paper - Volume 10 | Issue 11 | November 2025

IJNRD is a Scholarly Open Access, Peer-Reviewed, Refereed, and UGC CARE Journal Publication with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost, and Transparent Peer Review Journal Publication that adheres to the UGC CARE 2025 Peer-Reviewed Journal Policy and aligns with Scopus Journal Publication standards to ensure the highest level of research quality and credibility.

IJNRD offers comprehensive Journal Publication Services including indexing in all major databases and metadata repositories, Digital Object Identifier (Crossref DOI) assignment for each published article with additional fees, citation generation tools, and full Open Access visibility to enhance global research reach and citation impact.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse academic and professional fields. The journal promotes global knowledge exchange among researchers, developers, academicians, engineers, and practitioners, serving as a trusted platform for innovative, peer-reviewed journal publication and scientific collaboration.

Indexing Coverage: Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many other recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

You can now publish your research in IJNRD. IJNRD is a Transparent Peer-Reviewed Open Access Journal Publication (Refereed Journal), aligning with New UGC and UGC CARE recommendations.

For more details, refer to the official notice: UGC Public Notice

Submit Paper Online

Important Dates for Current issue

Paper Submission Open For: November 2025

Current Issue: Volume 10 | Issue 11 | November 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 30-Nov-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details

Approval, Licenses and Indexing: More Details