Document Detail

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.
MedLine Citation:
PMID:  25160237     Owner:  NLM     Status:  In-Data-Review    
Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.
Onur Erdoğan; Yeşim Aydin Son
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Studies in health technology and informatics     Volume:  205     ISSN:  0926-9630     ISO Abbreviation:  Stud Health Technol Inform     Publication Date:  2014  
Date Detail:
Created Date:  2014-08-27     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214582     Medline TA:  Stud Health Technol Inform     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  511-5     Citation Subset:  T    
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