A rough set data model for kidney disease diagnostics

• Main Author: Africa, Aaron Don M.
• Format: text
• Published: Animo Repository 2016
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/751
• Institution: De La Salle University

© Research India Publications. The human organ known as the kidney is very important to humans. These organs act to filter extra water out blood and other human waste. This organ helps control the blood pressure so the human body will stay healthy. There are instances when the human kidney does not function properly. This condition is known as kidney disease. Kidney damage can cause waste to build up in the body. If not treated properly and immediately, it may cause serious complications to the human body. One challenge is detecting if kidney disease is present. Early detection is a big factor in treating kidney disease. If the disease is detected early it may be easily treated unlike in later stages where complications may already occurred. A challenge in detecting it is finding the symptoms. The data of symptoms can be incomplete that a proper diagnosis cannot be given. This research solves that problem by creating a Rough Set Data Model for the diagnosis of kidney disease. It will use the UCI machine learning database as the primary data. Byusing this Rough Set Data Model it will now be possible to detect kidney disease even if there is incomplete information thereby facilitating in its early detection.