Heart Disease Detection using Iridology with Principal Component Analysis (PCA) and Backpropagation Neural Network

Leonardus Sandy Ade Putra, R. Rizal Isnanto, Aris Triwiyatno, Vincentius Abdi Gunawan

2018

Abstract

Heart is one of many vital organs on the human body which function is to pump blood throughout the body. Based on the data from World Health Organization (WHO), impaired heart function is the number one cause of death in the world. Early symptoms of heart disease commonly go unnoticed by the patients themselves and are often neglected. According to some circles on society, heart condition checking is assumed expensive, inconvenient, and takes a long time to do. A simpler and cheaper way to detect early heart complication symptom is needed. The iridology method can be used as a solution to resolve the problem above. Iridology is a method to determine abnormalities or complications that are happening on an organ's function by taking an image on iris as the main object of diagnosis. This research is done to make a system using image processing, feature extraction using Principal Component Analysis (PCA) and classification using Backpropagation Neural Network to recognize the condition of the heart's function. The researcher used 90 patient data with normal and abnormal heart condition. These data will be divided into 50 training data and 40 test data. Based on the test that has been done by using PCA score result variations as many as 600, 500, 400, 300 and 200, percentages of recognition rate have been obtained. The percentages in order are 92.5%, 90%, 85%, 75%, and 67.5%. The designed system can be used to detect early symptoms of heart function problem by using the Iridology method with the highest recognition rate of 92.5% using the PCA score of 600.

Paper Citation

in Harvard Style

Putra L., Isnanto R., Triwiyatno A. and Gunawan V. (2018). Heart Disease Detection using Iridology with Principal Component Analysis (PCA) and Backpropagation Neural Network.In Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology - Volume 1: EIC, ISBN 978-989-758-411-4, pages 257-264. DOI: 10.5220/0009009402570264

in Bibtex Style

@conference{eic18,
author={Leonardus Sandy Ade Putra and R. Rizal Isnanto and Aris Triwiyatno and Vincentius Abdi Gunawan},
title={Heart Disease Detection using Iridology with Principal Component Analysis (PCA) and Backpropagation Neural Network},
booktitle={Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology - Volume 1: EIC,},
year={2018},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009009402570264},
isbn={978-989-758-411-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology - Volume 1: EIC,
TI - Heart Disease Detection using Iridology with Principal Component Analysis (PCA) and Backpropagation Neural Network
SN - 978-989-758-411-4
AU - Putra L.
AU - Isnanto R.
AU - Triwiyatno A.
AU - Gunawan V.
PY - 2018
SP - 257
EP - 264
DO - 10.5220/0009009402570264