Application of Ensemble Learning Based Multivariate Pattern Analysis On Clinical Decision Support Systems

Year 2018, , 953 - 961, 01.08.2018

Ayşe Demirhan

https://doi.org/10.29130/dubited.432861

Cited By: 1

Abstract

Multivariable pattern analysis (MVPA) is a powerful technique that is used widely to examine clinical data. Machine learning methods are generally used in MVPA applications. Ensemble learning algorithms increase the classification performance by combining a large number of machine learning methods. Cervical cancer that is the second most common cancer type in women is a major cause of death in low-income countries. Cervical cancer, which has no symptoms in its initial stages, can be completely cured if it is diagnosed early. In this study a MVPA application is performed that uses random forests, extremely randomized trees and Adaboost ensemble learning methods to estimate the risk of cervical cancer of the patients by using the various risk factors for different screening strategies. Prediction performance of the extremely randomized trees for the cervical cancer risk was 99%, 96%, 97% and 97% for the Hinselmann, Schiller, citology and biopy screening methods.

Keywords

Ensemble learning algorithms, Random forests, Extremely randomized trees, Adaboost, Clinical decision support systems

Kolektif Öğrenmeye Dayalı Çok Değişkenli Desen Analizinin Klinik Karar Destek Sistemlerinde Uygulanması

Abstract

Çok değişkenli desen analizi (ÇDDA) klinik verilerin incelenmesi için
yaygın olarak kullanılan güçlü bir tekniktir. ÇDDA uygulamalarında genellikle
makine öğrenmesi yöntemleri kullanılmaktadır. Kolektif öğrenme algoritmaları çok
sayıda makine öğrenmesi metodunu bir araya getirilerek sınıflama performansını
arttırmaktadır. Kadınlarda en sık görülen ikinci kanser türü olan serviks
kanseri düşük gelirli ülkelerde önemli bir ölüm nedenidir. İlk evrelerinde belirti
göstermeyen serviks kanseri erken teşhis edildiğinde tamamen tedavi
edilebilmektedir. Bu çalışmada rastgele ormanlar, aşırı rassal ağaçlar ve
Adaboost kolektif öğrenme yöntemleri kullanılarak hastaların serviks kanseri
riskini çeşitli risk faktörlerinden faydalanarak farklı tarama yöntemleri
açısından tahmin eden bir ÇDDA uygulaması yapılmıştır. Aşırı rassal ağaçlar
algoritmasından Hinselmann, Schiller, sitoloji ve biyopsi tarama yöntemlerinin
hastaların serviks kanseri olması riskini tahmin başarısı sırasıyla %99, %96,
%97 ve %97 olmuştur.

Keywords

Kolektif öğrenme algoritmaları, Rastgele ormanlar, Aşırı rassal ağaçlar, Adaboost, Klinik karar destek sistemleri

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