Andere Kunden interessierten sich auch für
![Report on Cell Tower Radiation]( "Report on Cell Tower Radiation")
Report on Cell Tower Radiation16,99 €![Security Assistance System for Deaf and Dumb Using Gesture Recognition]( "Security Assistance System for Deaf and Dumb Using Gesture Recognition")
Security Assistance System for Deaf and Dumb Using Gesture Recognition26,99 €![Google Smart Contact Lens: Monitoring Diabetes from Tears]( "Google Smart Contact Lens: Monitoring Diabetes from Tears")
Google Smart Contact Lens: Monitoring Diabetes from Tears23,99 €![GSM and GPS Based Transportation Assistance System]( "GSM and GPS Based Transportation Assistance System")
GSM and GPS Based Transportation Assistance System36,99 €![FPGA Fuzzy (PD & PID) Controller Models for Insulin Pumps In Diabetes]( "FPGA Fuzzy (PD & PID) Controller Models for Insulin Pumps In Diabetes")
FPGA Fuzzy (PD & PID) Controller Models for Insulin Pumps In Diabetes32,99 €![FPGA Implementation of MIL-STD-1553B Bus Protocol]( "FPGA Implementation of MIL-STD-1553B Bus Protocol")
FPGA Implementation of MIL-STD-1553B Bus Protocol42,99 €![Zero ¿ emission bus transit]( "Zero ¿ emission bus transit")
Zero ¿ emission bus transit40,99 €
In Data Mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. To Apply Association Rule Mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. An Electronic Medical Record (EMR) is an evolving concept defined as a systematic collection of electronic health information about individual patients or population. The high dimensionality of EMR's, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. Applied four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. It is found that all four methods produced summaries that described subpopulations at high risk of diabetes with each method having itsclear strength. For our purpose, our extension to the Bottom-Up Summarization (BUS) algorithm produced the most suitable summary.