A rough set based data model for heart disease diagnostics

• Main Author: Africa, Aaron Don M.
• Format: text
• Published: Animo Repository 2016
• Subjects: Heart—Diseases—Diagnosis; Rough sets; Biomedical
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3426
• Institution: De La Salle University

Heart disease is one of the leading causes of death to human beings. This disease has taken numerous lives throughout human history. Heart disease describes a range of conditions that affects the heart. This disease refers to conditions that involve blocked blood vessels that can lead to a heart attack or stroke. Heart failure caused by damage to the heart that has developed over time cannot be cured. But it can be treated to improve its symptoms. In general, the earlier that a heart disease is detected the better options are available to diagnose it. This paper presented how Rough Set theory is applied to develop a data model to aid a physician to diagnose heart disease. In particular this research will utilize the data obtained from the Hungarian database UCI Machine Learning Repository. The results of the research showed that the rough set theory successfully reduced the dimensionality of the heart disease data set by approximately 49%. Empirical testing was used to validate the rules and gave a 100% result. © 2006-2016 Asian Research Publishing Network (ARPN).