Paper Info
Title
MIAT: A novel attribute selection approach to better predict upper gastrointestinal cancer
Abstract
The use of data mining has led to many significant medical discoveries. However, many challenges still exist in using these methods for knowledge discovery within this field given that the large amounts of data medical practitioners collect often creates a curse of dimensionality. To address this challenge, attribute selection approaches have been developed. However, current approaches typically put equal weight on all values within that attribute. At times, and especially within medical domains, we claim that these approaches might miss attributes where only a small subset of attribute values contain a strong indication for one of the target values and thus should still be selected. To quantify this approach, we present MIAT, an algorithm that defines Minority Interesting Attribute Thresholds to find these important attribute values. As we developed MIAT to help better diagnose upper gastrointestinal cancer, we present how we use the attributes selected through this approach to build a predictive model for this cancer. To demonstrate MIAT's generality, we also applied it to a canonical Hungarian Heart Disease Dataset. In both datasets we found that MIAT yields significantly better accuracy and sensitivity over traditional attribute selection approaches.
Year
DOI
Venue
2015
10.1109/DSAA.2015.7344866
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Keywords
DocType
ISBN
MIAT,attribute selection approach,upper gastrointestinal cancer,data mining,knowledge discovery,medical domain,minority interesting attribute threshold,canonical Hungarian heart disease dataset
Conference
978-1-4673-8272-4
Citations 
PageRank 
References 
2
0.38
7
Authors
9
Name
Order
Citations
PageRank
Avi Rosenfeld121624.80
David G. Graham240.76
Rifat Hamoudi331.43
Rommel Butawan420.38
Victor Eneh520.38
Saif Khan620.38
Haroon Miah720.38
Mahesan Niranjan8775120.43
Laurence B Lovat927315.66