Predicting the disease of alzheimer (AD) with SNP biomarkers and clinical data based decision support system using data mining classification approaches

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Enformatik Enstitüsü, Sağlık Bilişimi Anabilim Dalı, Türkiye

Tezin Onay Tarihi: 2012

Öğrenci: ONUR ERDOĞAN

Danışman: YEŞİM AYDIN SON

Özet:

Single Nucleotide Polymorphisms (SNPs) are the most common DNA sequence variations where only a single nucleotide (A, T, C, G) in the human genome differs between individuals. Besides being the main genetic reason behind individual phenotypic differences, SNP variations have the potential to exploit the molecular basis of many complex diseases. Association of SNPs subset with diseases and analysis of the genotyping data with clinical findings will provide practical and affordable methodologies for the prediction of diseases in clinical settings. So, there is a need to determine the SNP subsets and patients’ clinical data which is informative for the prediction or the diagnosis of the particular diseases. So far, there is no established approach for selecting the representative SNP subset and patients’ clinical data, and data mining methodology that is based on finding hidden and key patterns over huge databases. This approach have the highest potential for extracting the knowledge from genomic datasets and to select the number of SNPs and most effective clinical features for diseases that are informative and relevant for clinical diagnosis. In this study we have applied one of the widely used data mining classification methodology: “decision tree” for associating the SNP Biomarkers and 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 efficient and accurate tree for predicting the AD is presented.