Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Bayesian Additive Regression Trees for Predicting Colon Cancer: Methodological Study (Validity Study)
Kolon Kanserini Öngörmede Bayesian Eklemeli Regresyon Ağaçları: Metodolojik Çalışma (Geçerlik Çalışması)
Oyebayo Ridwan OLANIRANa, Saidat Fehintola OLANIRANb, Jumoke POPOOLAa, Ifeyinwa Vivian OMEKAMa
aDepartment of Statistics, Faculty of Physical Sciences, University of Ilorin, Ilorin, PMB 1515, Kwara State, Nigeria
bDepartment of Statistics and Mathematical Sciences, Faculty of Pure and Applied Science, Kwara State University, PMB 1530 Malete, Kwara State, Nigeria
Turkiye Klinikleri J Biostat. 2022;14(2):103-9
doi: 10.5336/biostatic.2022-89894
Article Language: EN
Full Text
ABSTRACT
 Objective: The occurrence of colon cancer starts in the inner wall of the large intestine. The survival of colon cancer patients strongly relies on early detection. Diagnosing colon cancer using clinical approaches often takes longer, especially in most developing countries with limited facilities. The recent use of microarray technology has presented a new approach for the oncologist to diagnose cancer cells using non-clinical machine learning methods. In this paper, the aim is to predict the status of colon cancer tissues using the Bayesian Additive Regression Trees (BART) and 2 other machine learning methods. Material and Methods: The development and comparative analysis of BART alongside 2 other competing methods (Random Forest: RF and Gradient Boosting Machine: GBM) were implemented. The dataset used for the analysis is the microarray colon cancer data which consists of 2,000 gene expression measurements for 62 tissue samples. Results: The methods are compared based on overall metrics (accuracy, balance accuracy, detection rate, F-measure and AUC) and class-specific metrics (sensitivity, specificity, positive predictive value and negative predictive value). The overall metrics results showed that the best method is RF. The class-specific metrics results showed that BART is better than RF. Conclusion: On average, BART is more sensitive in detecting the presence of colon cancer cells, while RF is more accurate and specific in detecting the presence or absence of colon cancer cells.

Keywords: Colon cancer; Bayesian trees; random forest; gradient boosting
ÖZET
 Amaç: Kolon kanseri kalın bağırsağın iç duvarında başlar. Kolon kanseri hastalarının sağ kalımı kuvvetle erken tanıya dayanır. Kolon kanserine klinik yaklaşımlarla tanı koyulması özellikle sınırlı kaynakları olan gelişmekte olan ülkelerde sıklıkla uzun zaman alır. Son zamanlarda mikrodizilim teknolojisinin kullanımı onkologlara klinik olmayan makine öğrenme yöntemleri kullanılarak kanser hücrelerini tanımaları için yeni bir yaklaşım sunmaktadır. Bu yazının amacı Bayesian Eklemeli Regresyon Ağaçları [ Bayesian Additive Regression Trees (BART) ve diğer 2 makine öğrenme yöntemi kullanılarak kolon kanseri dokularının durumunun öngörülmesidir. Gereç ve Yöntemler: Diğer 2 hesaplama yöntemi olan Rastgele Orman (Random Forest: RF) ve Gradyan Artırma Makinesi (Gradient Boosting Machine: GBM) yanı sıra BART'ın geliştirilmesi ve karşılaştırmalı analizi uygulandı. Analiz için kullanılan veri seti, 62 doku örneği için 2.000 gen ekspresyon ölçümünden oluşan mikrodizi kolon kanseri verisidir. Bulgular: Yöntemler, genel ölçülere (doğruluk, terazi denge doğruluğu, saptama oranı, F-ölçüm ve AUC) ve sınıfa özgü ölçülere (duyarlılık, özgüllük, pozitif tahmin değeri ve negatif tahmin değeri) dayalı olarak karşılaştırıldı. Genel ölçüm sonuçları, en iyi yöntemin RF olduğunu göstermiştir. Sınıfa özel ölçü sonuçları, BART'ın RF'den daha iyi olduğunu göstermiştir. Sonuç: Ortalama olarak, BART kolon kanseri hücrelerinin varlığını tespit etmede daha duyarlıyken, RF kolon kanseri hücrelerinin varlığını veya yokluğunu tespit etmede daha doğru ve özgüldür.

Anahtar Kelimeler: Kolon kanseri; Bayesian ağaçları; rastgele orman; gradyan artırma
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