Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique

Title

Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique

Creator

Malarkodi P.; Arun M.; Manikandan R.; Ramkumar S.

Description

Cardiovascular diseases (CVDs) pose a significant global health challenge. Early and accurate diagnosis is crucial for effective treatment. This research focuses on developing a robust classification system for CVDs using machine learning techniques. This study proposes an enhanced Random Forest (RF) model optimized for big data environments and explore the potential of CNN-based classification. By leveraging medical imaging data and employing these advanced algorithms, we aim to improve the accuracy and efficiency of CVD diagnosis. 2024 IEEE.

Source

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024 - Proceedings, pp. 1598-1603.

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Big Data Architecture; Cardiac Disease Analysis; Convolution Neural Network (CNN); Deep Learning Algorithm; Image Processing; Machine Learning-Random Forest (ML-RF)

Coverage

Malarkodi P., Kalasalingam Academy of Research and Education, Department of Computer Applications, Tamilnadu, Krishnankoil, India; Arun M., Kalasalingam Academy of Research and Education, Department of Computer Applications, Tamilnadu, Krishnankoil, India; Manikandan R., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Ramkumar S., School of Sciences, Christ University, Karnataka, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835037994-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICESC60852.2024.10689896

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206639694&doi=10.1109%2fICESC60852.2024.10689896&partnerID=40&md5=0d01dabfbe59ef475b494e69e943a91d