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 : 10

Issue Published : 115

Article Submitted : 19459

Article Published : 8041

Total Authors : 21252

Total Reviewer : 769

Total Countries : 145

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: AI-Driven Heart Disease Prediction using Machine Learning
Authors Name: Subalakshmi R , Senthil vel S , Priyanka S , Dharun D , Vignesh N
Download E-Certificate: Download
Author Reg. ID:
IJRTI_202985
Published Paper Id: IJRTI2504297
Published In: Volume 10 Issue 4, April-2025
DOI:
Abstract: Heart-disease (HD) is one of the most common diseases nowadays, and for people who provide health care, it is very necessary to work with them to take care of their patients' health and save their life. In this paper, different classifiers were analyzed by performance comparison to classify the Heart Disease dataset to classify it correctly and or to Predict Heart Disease cases with minimal attributes. Large amounts of data that contain some secret information were collected by the healthcare industries. This data collection is useful for making effective decisions. Some advanced data mining techniques are used to make proper results and making effective decisions on data. In this case, a Heart Disease Prediction System (HDPS) is developed using Logistic Regression, K Nearest Neighbor, Decision Tree, Random Forest Classifier, and Support Vector Machine algorithms to predict the heart disease risk level. The results reveal that the Random Forest Classifier and Support Vector Machine obtained the highest accuracy of 90.32%, whereas 87.09%, 70.96%, and 83.87% accuracy scores are obtained by logistic regression, KNN classifier, and decision tree respectively.
Keywords: Heart Disease Prediction, Machine Learning, Web-Based System, Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, Decision Tree, Doctor Appointment System, Healthcare Technology, Medical Data Analysis.
Cite Article: "AI-Driven Heart Disease Prediction using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c625-c630, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504297.pdf
Downloads: 000318
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: IJRTI2504297
Registration ID:202985
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: c625-c630
Country: -, -, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504297
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504297
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

Join RMS/Earn 300

IJRTI

WhatsApp
Click Here

Indexing Partner