Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Tech Science Press
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution 3.0 United States
Attribution 3.0 United States
Özet
The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyperparameter optimization to get the best performance from different models, which is overlooked in most studies, was another objective of the study.Within the framework of these motivations, C4.5, random forest (RF), artificial neural networks (ANN), and deep learning (DL) were used. In this way, the diagnostic performance of decision tree (DT)-based models and black box models on BCa was also compared. The most successful model, DL, yielded an area under the curve (AUC) of 0.985 and a mean square error (MSE) of 0.069. For each model, hyper-parameters were optimized by an evolutionary algorithm. On average, hyper-parameter optimization increased MSE, root mean square error (RMSE), LogLoss, and AUC by 30%, 17.5%, 13%, and 6.75%, respectively. The features causing BCa were extracted. For this purpose, entropy and Gini coefficients were used for DT-based methods, and the Gedeon variable importance was used for black box methods. The single nucleotide polymorphisms (SNPs) rs197412, rs2275928, rs12479919, rs798766 and rs2275928, whose BCa relations were proven in the literature, were found to be closely related to BCa. In addition, rs1994624 and rs2241766 susceptibility loci were proposed to be examined in future studies.
Açıklama
Anahtar Kelimeler
Bladder cancer, Deep learning, Hyper-parameter optimization, Neural network, Random forest, Single nucleotide polymorphism
Kaynak
Intelligent Automation and Soft Computing
WoS Q Değeri
Q3
Scopus Q Değeri
Cilt
37
Sayı
1
Künye
Yücebaş, S.C. (2023). Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer. Intelligent Automation & Soft Computing, 37(1), 49–71. https://doi.org/10.32604/iasc.2023.036871
