Heart Disease Classification using PCA and Back-Propagation Neural Network

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.6 · Issue 10

Published

May 2018

Access

Open Access

Licence

CC BY-NC-SA 4.0

Shivani Sawai Aanchal Koul Antara Rangnekar

Cardiovascular diseases are the leading cause of disability and premature death worldwide, and contribute substantially to the rising costs of health care. The fundamental anatomy pathological lesion is atherosclerosis, which occurs over the years and is usually advanced when symptoms appear, usually at maturity. Acute coronary and cerebrovascular events often occur suddenly and are often fatal before medical attention can be provided. It has been shown that the modification of risk factors reduces mortality and morbidity in people with cardiovascular diseases, diagnosed or not. The main objective of this research work is to develop a prototype which can determine and extract unknown knowledge (patterns and relations) related with heart disease from a past heart disease database record. This paper uses Neural Networks Algorithm technique for heart disease prediction. PCA is used to reduce number of attributes which indirectly reduces the no. of diagnosis tests which are needed to be taken by a patient. Performance of proposed approach is evaluated using confusion matrix plot.

Shivani Sawai, Aanchal Koul, Antara Rangnekar (2018). Heart Disease Classification using PCA and Back-Propagation Neural Network. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.6, Issue 10. ISSN: 2319-4863.

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