Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  A Hybrid Deep Learning Model for Heart Disease Risk Assessment
PaperId: :  22827
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22827
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Kaustubh SavallaVasireddy Venkatadri Institute of Technology
Sai Nikhil MopideviVasireddy Venkatadri Institute of Technology
Madhan Kumar MunagalaVasireddy Venkatadri Institute of Technology
Brahma Naidu MadineediVasireddy Venkatadri Institute of Technology

Abstract

Deep Learning
Heart Disease, ECG, Arrythmia, EfficientNetB0, Hybrid deep learning.
Heart diseases remains a leading cause of mortality worldwide, necessitating effective risk assessment methodologies. In this study, we propose a comprehensive approach utilizing both clinical data and electrocardiogram (ECG) images to assess heart disease risk. Our dataset comprises clinical variables such as age, gender, blood pressure, cholesterol levels, along with ECG images depicting various cardiac arrhythmias including ventricular fibrillation (VFib), premature atrial contractions (PAC), premature ventricular contractions (PVC), left bundle branch block (LBBB), and right bundle branch block (RBBB). We employ various Machine Learning models and deep learning architectures, a hybrid model. For the analysis of ECG images, we leverage the power of convolutional neural networks (CNNs) with EfficientNetB0 architecture, known for its efficiency and effectiveness in image classification tasks. Through experimentation and evaluation, we demonstrate the performance of these models in predicting heart disease risk. This project provides insights into the potential of integrating clinical data and ECG images for enhanced risk assessment, contributing to the development of more accurate and personalized diagnostic tools in cardiology.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. "A Hybrid Deep Learning Model for Heart Disease Risk Assessment" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 786-792
MLA Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. "A Hybrid Deep Learning Model for Heart Disease Risk Assessment." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 786-792.
APA Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, & Brahma Naidu Madineedi. (2024). A Hybrid Deep Learning Model for Heart Disease Risk Assessment. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 786-792.
Chicago Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. "A Hybrid Deep Learning Model for Heart Disease Risk Assessment." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 786-792.
Oxford Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. 'A Hybrid Deep Learning Model for Heart Disease Risk Assessment', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 786-792. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/A_Hybrid_Deep_Learning_Model_for_Heart_Disease_Risk_Assessment_ijariie22827.pdf (Accessed : 20 March 2024).
Harvard Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. (2024) 'A Hybrid Deep Learning Model for Heart Disease Risk Assessment', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 786-792IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/A_Hybrid_Deep_Learning_Model_for_Heart_Disease_Risk_Assessment_ijariie22827.pdf (Accessed : 20 March 2024)
IEEE Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi, "A Hybrid Deep Learning Model for Heart Disease Risk Assessment," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 786-792, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/A_Hybrid_Deep_Learning_Model_for_Heart_Disease_Risk_Assessment_ijariie22827.pdf [Accessed : 20 March 2024].
Turabian Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. "A Hybrid Deep Learning Model for Heart Disease Risk Assessment." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (20 March 2024).
Vancouver Kaustubh Savalla, Sai Nikhil Mopidevi, Madhan Kumar Munagala, and Brahma Naidu Madineedi. A Hybrid Deep Learning Model for Heart Disease Risk Assessment. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 20 March 2024]; 10(2) : 786-792. Available from: https://ijariie.com/AdminUploadPdf/A_Hybrid_Deep_Learning_Model_for_Heart_Disease_Risk_Assessment_ijariie22827.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 3/20/2024 10:41:36 AM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
SOLAR WIRELESS ELECTRIC VEHICLE USING CHARGING SYSTEMElectronic and communication Hima J Download
Literature Review on Fibre Bragg Grating Sensors: Principles, Fabrication Techniques, Applications, and Future ProspectsElectronics and Communication Engineering Yashaswini D Download
Literature Survey on Smart Optical Fiber based Respiratory Monitoring systemElectronics and communication engineering Sai Priyanka A M Download
An loT Based Novel Approach to Health Emergency Management Through Real-Time Data Acquisition and AnalysisHealth sectorDr Siddesh G K Download
DESIGN AND DEVELOPMENT OF RIVER SURFACE CLEANING ROBOT USING IOTElectronics and communication engineeringBALAJI B R Download
A Solar Powered Wireless Power Transfer for Electric Vehicle (EV) ChargingElectronics and Communication EngineeringDr. Siddesh G K Download
AI-XRAY: AUTOMATED LUNG CANCER AND PNEUMONIA DETECTIONElectronics and communication engineeringYASHASWINI H M Download
Predictive Health Monitoring for Proactive Public Safety and ManagementElectronics and Communication EngineeringROOPA G D Download
IMPROVING BRAIN CANCER DETECTION THROUGH AI DRIVEN OBJECT RECOGNAISATIONElectronic and communication Parvathi CM Download
IOT BASED SMART FOOTWEAR FOR WOMEN & CHILDEREN SECURITYElectronics & CommunicationNandini G R Download
Smart Pesticide Sprayer Robot Using IOT In Agriculture FieldEletronics and Communication EngineeringMadhukeshava M Download
Evaluation of LoRa Based Air Pollution Monitoring SystemElectronic and Communication EngineeringSATHISH H Download
SMART CROP MONITORING SYSTEM AND IOTElectronics and Communication EngineeringSangeetha M R Download
Design of a IoT-based Water Harvesting, Moisture Monitoring, and Crop Monitoring System for Precision AgricultureElectronics and communication engineeringDeepthi T R Download
SMART GLOVES FOR SPEECH IMPAIRED PEOPLESELECTRONICS AND COMMUNICATION ENGINEERINGSudharshan M K Download
12