Early Detection of Cardiac Disease Using Machine Learning

6 Pages Posted: 22 Apr 2019

See all articles by Paras Chavda

Paras Chavda

University of Mumbai, K. J. Somaiya Institute of Engineering and Information Technology, Students

Harsh Bhavsar

University of Mumbai, K. J. Somaiya Institute of Engineering and Information Technology, Students

Yash Pithadia

University of Mumbai, K. J. Somaiya Institute of Engineering and Information Technology, Students

Radhika Kotecha

University of Mumbai - Department of Information Technology

Date Written: April 9, 2019

Abstract

Heart related diseases are primarily the main reason of death throughout the world and due to which a large number of casualties are arising in countries with low and middle income like India. A large amount of data is continuously generated by medical practitioners. The data generated can be used for the early detection of cardiac diseases, which can effectively support to reduce the occurrence of various heart related diseases. The decision prediction can be effectively done by enhancing the knowledge identification required to discover patterns that were not formerly known.

Efficient prediction can be done by accessing the data accumulated from health care companies and industries and find the hidden patterns. The proposed work uses a machine learning algorithm on cardiac-related data and attempts to detect the possibility of cardiac diseases prior to suffering from serious issues. Implementation results demonstrated in the paper show the effectiveness of the proposed approach in early prediction of cardiac diseases

Keywords: Cardiac Disease, Classification, Decision Tree, Machine Learning, IoT

Suggested Citation

Chavda, Paras and Bhavsar, Harsh and Pithadia, Yash and Kotecha, Radhika, Early Detection of Cardiac Disease Using Machine Learning (April 9, 2019). 2nd International Conference on Advances in Science & Technology (ICAST) 2019 on 8th, 9th April 2019 by K J Somaiya Institute of Engineering & Information Technology, Mumbai, India. Available at SSRN: https://ssrn.com/abstract=3370813 or http://dx.doi.org/10.2139/ssrn.3370813

Paras Chavda (Contact Author)

University of Mumbai, K. J. Somaiya Institute of Engineering and Information Technology, Students ( email )

Mumbai
India

Harsh Bhavsar

University of Mumbai, K. J. Somaiya Institute of Engineering and Information Technology, Students ( email )

Mumbai
India

Yash Pithadia

University of Mumbai, K. J. Somaiya Institute of Engineering and Information Technology, Students ( email )

Mumbai
India

Radhika Kotecha

University of Mumbai - Department of Information Technology ( email )

Somaiya Ayurvihar Complex
Eastern Express Highway
Mumbai, 400022
India

Register to save articles to
your library

Register

Paper statistics

Downloads
26
Abstract Views
114
PlumX Metrics