Gen Verileri Üzerinde İlginçlik Ölçütleri Kullanılarak Birliktelik Kuralları Madenciliğinin Uygulanması

Öz

Amaç: Veri madenciliği, önceden büyük ölçekli verilerden ortaya çıkarılmayan faydalı bilgilerin keşfedilme sürecidir. Veri madenciliğinin yaygın olarak kullanıldığı alanlardan biri de sağlıktır. Veri madenciliği ile hastalığın tanı ve tedavisi ile hastalığı etkileyen risk faktörleri hızlı bir şekilde belirlenebilmektedir. Birliktelik kuralları, veri madenciliği tekniklerinden biridir. Bu çalışmanın amacı, birliktelik kuralı algoritmalarından biri olan apriori algoritması ile güçlü birliktelik kuralları elde ederek hasta profillerini belirlemektir. Materyal ve Metot: Çalışmada kullanılan veri seti 205 akut miyokard enfarktüsü (AMI) hastasından oluşmaktadır. Hastalar ayrıca FNDC5 polimorfizmlerinin rs3480, rs726344, rs16835198 genotipini de taşımaktadır. Apriori algoritması ile elde edilen kuralları değerlendirmek için destek ve güven ölçüleri kullanılır. Ancak bu ölçütler ile elde edilen kurallar doğrudur ancak güçlü değildir. Bu nedenle, daha güçlü kurallar elde etmek amacıyla iki temel ölçütün yanı sıra ilginçlik ölçütleri kullanılmaktadır. Bu çalışmada daha güçlü kurallara ulaşmak için ilginçlik ölçütlerinden kaldıraç, kanaat, kesinlik faktörü, cosine, korelasyon katsayısı (phi) ve karşılıklı bilgi ölçütleri uygulanmıştır. Bulgular: Çalışmada 108 kural elde edilmiştir. Bu kurallara ilginçlik ölçütlerinin de uygulanması ile elde edilen kural sayısı 29 olmuştur ve bu kurallar güçlü kural olarak nitelendirilmiştir. Sonuç: Sonuç olarak, klinik karar verme sürecinde ilginçlik ölçütlerinin kullanılmasıyla daha güçlü kurallar elde edilmiştir. Elde edilen güçlü kurallar sayesinde AMİ hastalarının hasta profili belirleme ve klinik karar verme sürecini kolaylaştıracaktır.

Anahtar Kelimeler

• Veri madenciliği
• birliktelik kuralları
• apriori algoritması
• ilginçlik ölçütleri
• gen ifadesi verisi

Assessment of Association Rule Mining Using Interest Measures on the Gene Data

Abstract

Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One of the fields in which data mining is widely used is health. With data mining, the diagnosis and treatment of the disease and the risk factors affecting the disease can be determined quickly. Association rules are one of the data mining techniques. The aim of this study is to determine patient profiles by obtaining strong association rules with the apriori algorithm, which is one of the association rule algorithms. Material and Method: The data set used in the study consists of 205 acute myocardial infarction (AMI) patients. The patients have also carried the genotype of the FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support and confidence measures are used to evaluate the rules obtained in the Apriori algorithm. The rules obtained by these measures are correct but not strong. Therefore, interest measures are used, besides two basic measures, with the aim of obtaining stronger rules. In this study For reaching stronger rules, interest measures lift, conviction, certainty factor, cosine, phi and mutual information are applied. Results: In this study, 108 rules were obtained. The proposed interest measures were implemented to reach stronger rules and as a result 29 of the rules were qualified as strong. Conclusion: As a result, stronger rules have been obtained with the use of interest measures in the clinical decision making process. Thanks to the strong rules obtained, it will facilitate the patient profile determination and clinical decision-making process of AMI patients.

Keywords

• Data Mining
• Association Rules
• Apriori Algorithm
• Interest Measures
• Gene Expression Data

Kaynakça

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