ECTracker - An Efficient Algorithm for Haplotype Analysis and Classification
Conference Proceeding Article
This work aims at discovering the genetic variations of hemophilia A patients through examining the combination of molecular haplotypes present in hemophilia A and normal local populations using data mining methods. Data mining methods that are capable of extracting understandable and expressive patterns and also capable of making predictions based on inferences made on the patterns were explored in this work. An algorithm known as ECTracker is proposed and its performance compared with some common data mining methods such as artificial neural network, support vector machine, naive Bayesian, and decision tree (C4.5). Experimental studies and analyses show that ECTracker has comparatively good predictive accuracies in classification when compared to methods that can only perform classification. At the same time, ECTracker is also capable of producing easily comprehensible and expressive patterns for analytical purposes by experts.
Datamining, Classification, Hemophilia A; Genetic variations, Haplotypes
Computer Sciences | Health Information Technology
Data Management and Analytics
MEDINFO 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics
I O S PRESS
City or Country
Lin, Li; Wong, Limsoon; Tze-Yun LEONG; and Lai Pohsan.
ECTracker - An Efficient Algorithm for Haplotype Analysis and Classification. (2007). MEDINFO 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics. 129, 1270-1274. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3056