 |
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)
|
Issue: March 2026
Volume 11 | Issue 3
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 : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21664
Article Published : 8541
Total Authors : 22459
Total Reviewer : 811
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Heart Disease Prediction Using Deep Learning LSTM |
| Authors Name: | Palwe Pooja Balasaheb |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204704
|
| Published Paper Id: | IJRTI2506084 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | Heart disease is still one of the top reasons people die around the globe, which makes it clear that we really need better ways to predict it in healthcare. This study presents a deep learning method using Long Short-Term Memory (LSTM) networks to forecast heart disease, and it includes tracking past predictions to help make smarter decisions. The approach starts by gathering data from patient health records. Then, this data goes through important preprocessing steps like cleaning, normalizing, fixing missing values, and extracting features to ensure its high quality for our predictive model. After that, the polished data moves through a deep learning pipeline made up of sequential LSTM layers, dense layers, and dropout layers, all aimed at making learning more efficient and boosting prediction accuracy. The model can provide real-time predictions, which we keep stored for historical analysis and visualize using a special module. This way, healthcare professionals can keep an eye on patient health trends over time and make well-knowledgeable decisions. Finally, the system is put into action in a real-world medical setting, working alongside decision support tools to help doctors figure out and manage heart disease risks. By using deep learning and analysing time-series data, this framework improves prediction reliability and helps with early diagnosis, finally leading to proactive healthcare measures that can lessen the impact of cardiovascular diseases. |
| Keywords: | |
| Cite Article: | "Heart Disease Prediction Using Deep Learning LSTM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a706-a713, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506084.pdf |
| Downloads: | 000427 |
| 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: IJRTI2506084
Registration ID:204704
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: a706-a713 Country: Wagholi, Pune, Maharashtra, India Research Area: Biological Science Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506084 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506084 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
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
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||