IJRTI
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 9

Issue Published : 93

Article Submitted : 9866

Article Published : 5018

Total Authors : 13224

Total Reviewer : 557

Total Countries : 93

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: ACTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING HYBRID LEARNING STRATEGIES
Authors Name: Rachana T , Gowthami H R , M Rama krishna , Apoorva R
Download E-Certificate: Download
Author Reg. ID:
IJRTI_181045
Published Paper Id: IJRTI1911009
Published In: Volume 4 Issue 11, November-2019
DOI:
Abstract: Heart disease is one of the most important causes of death in the world today, Prediction for cardiovascular disease is a key problem in the world of clinical data analysis Machine learning(ML) has been shown to be effective in helping to make decisions and predictions based on the large amount of data produced by the healthcare industry. We also saw the use of ML techniques Heart disease is one of the most important causes of death in the world today, Prediction for cardiovascular disease is a key problem in the world of clinical data analysis machine learning(ML) has been shown to be effective in helping to make decisions and predictions based on the large amount of data produced by the healthcare industry, We also saw the use of ML techniques used in recent developments in different areas of the Internet of Things (IoT), Various studies provide only insight into detecting heart disease using ML techniques.
Keywords: Decision tree, KNN, Machine learning.
Cite Article: "ACTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING HYBRID LEARNING STRATEGIES", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 11, page no.41 - 45, November-2019, Available :http://www.ijrti.org/papers/IJRTI1911009.pdf
Downloads: 000202620
ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI1911009
Registration ID:181045
Published In: Volume 4 Issue 11, November-2019
DOI (Digital Object Identifier):
Page No: 41 - 45
Country: Moodbidri, Karnataka, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI1911009
Published Paper PDF: https://www.ijrti.org/papers/IJRTI1911009
Share Article:

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI.ONE
How to Get DOI?

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Join RMS/Earn 300

IJRTI

Indexing Partner