Karmaşık Hastalıkların Teşhisinde Veri Madenciliği Yöntemlerinin Başarım Karşılaştırması

Abstract

Bütünsel genom ilişkilendirme çalışmalarında (BGİÇ) ortaya çıkan verilerin yüksek miktarda ve çok boyutlu olması, profillerin hastalıklarla ilişkilendirilmesi ve buradan teşhise gidilmesi sırasında farklı veri madenciliği yöntemlerinin kullanılması ile mümkün olmaktadır. Yapılan çalışmada 1025 vaka ve 531 kontrolden oluşan melonom veri kümesi ile farklı etnik kökenli 2325 vaka ve 2350 kontrolden oluşan ve prostat kanseri veri kümesi kullanılmıştır. Bu hastalıklarla ilgili profiller Karar Ağacı, Naive Bayes, Destek Vektör Makinası gibi farklı veri madenciliği yöntemleri ile incelenmiştir. Her iki hastalık için de destek vektör makinası kullanılan yöntemler arasında en iyi başarımı sağlamıştır. İlgili yöntem prostat kanseri veri kümesinde %75.68’lık bir kesinlik değeri sunarken, melonom veri kümesi için %78,6’lik bir kesinlik değeri yakalamıştır. 

Keywords

• veri madenciliği,karar ağacı,destek vektör makinesi,naive bayes,kanser,bütünsel genom ilişkilendirme

Performance Comparison of Data Mining Methods in Diagnosis of Complex Diseases

Abstract

The data used in Genome Wide Association studies is vast in amount and high dimensional. Therefore, different data mining methods are used in order to find the relations between profiles and diseases. These methods are then used for diagnostic models. In this study two different data sets were used. The melonoma data set consists of 1025 cases and 531 controls. The multi ethnic prostate cancer data set consists of 2325 cases and 2350 controls. The underlying SNPs were searched by different data mining methods such as Decision Trees, Naive Bayes and Support Vector Machines. For both diseases support vector machine presented the best performance results. This method presented 75.68% of accuracy for prostate cancer data and 78.6% of accuracy for melonoma.

 



Keywords

• data mining,decision tree,support vector machine,naive bayes,cancer,genome wide association

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