|
|||||||||||||||
|
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) |
||||||||||||||
Issue: May 2024
Volume 9 | Issue 5
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 96
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Enhancing Heart Disease Prediction Models through Data-driven Feature Selection: A Mafia-K-means Fusion Approach |
| Authors Name: | PETER DASSU , DR. K.M. Abubakkar sithik |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_213423
|
| Published Paper Id: | IJNRD2402087 |
| Published In: | Volume 9 Issue 2, February-2024 |
| DOI: | |
| Abstract: | Heart disease remains a significant global health concern, necessitating accurate predictive models for timely diagnosis and intervention. In this paper, we propose a novel approach, merging Mafia and K-means algorithms, aimed at improving feature selection for predictive models. Leveraging the robustness of Mafia algorithms and the clustering efficacy of K-means, we iteratively select informative features from complex datasets related to heart health. Our fusion technique demonstrates superior predictive performance compared to traditional methods, showcasing enhanced accuracy, precision, and recall rates. The results underscore the potential of this approach in refining heart disease prediction models, offering a promising avenue for future research in medical data analysis and predictive |
| Keywords: | Heart disease, Mafia, K-means algorithm, clustering |
| Cite Article: | "Enhancing Heart Disease Prediction Models through Data-driven Feature Selection: A Mafia-K-means Fusion Approach", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 2, page no.a767-a773, February-2024, Available :http://www.ijnrd.org/papers/IJNRD2402087.pdf |
| Downloads: | 000118766 |
| 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 |
| Publication Details: |
Published Paper ID:IJNRD2402087 Registration ID: 213423 Published In: Volume 9 Issue 2, February-2024 DOI (Digital Object Identifier): Page No: a767-a773 Country: BLANTYRE, BLANTYRE, Malawi Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2402087 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2402087 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn