Interpretable risk stratification for CVD using random forest and XGBoost

Registration ID: IJNRD_309002

Published ID: IJNRD2508205

: Click Here to Get

Pooja K, Dr.Mohamed Rafi, Sohan U R

Cardiovascular Disease Prediction, Explainable AI (XAI), SHAP, Random Forest, XGBoost, Predictive Analytics

Cardiovascular diseases (CVDs) continue to be the predominant cause of global mortality, responsible for nearly 18 million deaths annually, and a rising incidence is observed due to sedentary lifestyles, inappropriate dietary patterns, and high stress levels in modern life. In The need for technology in recent years has increased in real-time, accurate, and comprehensible forecasting systems has grown exponentially, especially in clinical situations where early decisions are critical. This study offers a real-time prediction model of cardiovascular disease with machine learning models, i.e., Random Forest and XGBoost, which have been trained on the cardio_train.csv dataset with high lifestyle and medical indicators. To counter the disadvantage of non-interpretability of black-box models, the system leverages Explainable Artificial Intelligence (XAI) models where attention is provided to SHAP (SHapley Additive exPlanations) in a bid to offer transparent decision-making for medical practitioners. The model achieves outstanding predictive performance and performs well in real-time applications, not only delivering risk scores but even explanations for each and every prediction. This synergy of prediction and explainability facilitates proactive clinical interventions and harmonizes with the increasing emphasis on personalized, transparent, and AI-assisted healthcare systems in the post-pandemic world.

Published Paper   E-Certificate

Pooja K, Dr.Mohamed Rafi, & Sohan U R (August-2025). Interpretable risk stratification for CVD using random forest and XGBoost. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(8), c19-c25. https://ijnrd.org/papers/IJNRD2508205.pdf

Volume 10 Issue 8, August-2025

Pages : c19-c25

Paper Reg. ID: IJNRD_309002

Published Paper Id: IJNRD2508205

Downloads: 000121990

Research Area: Science All

Author Type: Indian Author

Country: DAVANAGERE, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2508205.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2508205

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