Karmaşık Hastalıkların Teşhisinde Veri Madenciliği Yöntemlerinin Başarım Karşılaştırması

Year 2018, Volume: 4 Issue: 1, 14 - 27, 17.05.2018

Sait Can Yücebaş

https://doi.org/10.28979/comufbed.395117

Cited By: 1

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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