Development of an expert system algorithm for diagnosing cardiovascular disease using rough set theory implemented in MATLAB
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Document Type
Article
Source Title
ARPN Journal of Engineering and Applied Sciences
Volume
12
Issue
23
First Page
6920
Last Page
6925
Publication Date
12-1-2017
Abstract
Cardiovascular disease refers to conditions that involve narrow or blocked blood vessels. This disease when remained untreated may lead to a heart attack. When a person has a cardiovascular disease, The heart may not be able to pump enough blood to the body. When there is insufficient blood the brain or other organs may become damaged. Cardiovascular disease is challenging to diagnose because its symptoms may be mistaken for other diseases. Early detection, if a person has cardiovascular disease is a big advantage in combating the ailment. This is because diagnosing the disease early may reduce the complications it may bring. This research will develop an Expert System Algorithm for the diagnosis of cardiovascular disease. This research will guide the person diagnosing the disease to provide the appropriate recommendation. The Rough Set Theory will be used to reduce the rules so it can be easily diagnosed. This research will utilize the Statlog Heart Data Set of the UCI machine learning repository. Matrix Laboratory or MATLAB will be used to implement the system. © 2006-2017 Asian Research Publishing Network (ARPN).
html
Recommended Citation
Africa, A. M. (2017). Development of an expert system algorithm for diagnosing cardiovascular disease using rough set theory implemented in MATLAB. ARPN Journal of Engineering and Applied Sciences, 12 (23), 6920-6925. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/2514
Disciplines
Biomedical Devices and Instrumentation | Electrical and Computer Engineering
Keywords
Cardiovascular system—Diseases—Diagnosis; Expert systems (Computer science); Biomedical engineering; Rough sets
Upload File
wf_no