Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM47696
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Heart disease is one of the most rapidly growing diseases worldwide. Detection of artery disease in the early stages is very crucial for providing the right treatment at the right time to escape from any kind of bigger risks, this is where there has been a need of an advanced system that can detect the artery disease to get a control over it at the right time. This paper proposes a system for the medical professionals, a web application platform developed with MERN Stack for preprocessing the medical data with techniques like Regression, Random Forest, and Gradient Boosting to enhance the data quality and then processing the data by utilizing convolutional neural network machine learning model like VGG16 and ResNet50 to classify the medical data as risk or no-risk. The convolutional neural network machine learning model is trained by publicly available and clinically validated datasets such as ISIC Archive and HAM10000 for achieving better accuracy and precision.
KEYWORDS
Publication Date
12/4/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...