Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer

Yücebaş, Sait Can

Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer

dc.authorid	0000-0002-1030-3545	en_US
dc.authorscopusid	24491277000	en_US
dc.authorwosid	JAN-6981-2023	en_US
dc.contributor.author	Yücebaş, Sait Can
dc.date.accessioned	2024-01-16T11:59:45Z
dc.date.available	2024-01-16T11:59:45Z
dc.date.issued	2023	en_US
dc.department	Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	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.	en_US
dc.identifier.citation	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	en_US
dc.identifier.doi	10.32604/iasc.2023.036871
dc.identifier.endpage	71	en_US
dc.identifier.issn	1079-8587
dc.identifier.issn	2326-005X
dc.identifier.issue	1	en_US
dc.identifier.scopus	2-s2.0-85160436865
dc.identifier.startpage	49	en_US
dc.identifier.uri	https://doi.org/10.32604/iasc.2023.036871
dc.identifier.uri	https://hdl.handle.net/20.500.12428/5257
dc.identifier.volume	37	en_US
dc.identifier.wos	WOS:000993115400004
dc.identifier.wosquality	Q3
dc.indekslendigikaynak	Web of Science
dc.indekslendigikaynak	Scopus
dc.institutionauthor	Yücebaş, Sait Can
dc.language.iso	en
dc.publisher	Tech Science Press	en_US
dc.relation.ispartof	Intelligent Automation and Soft Computing	en_US
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı	en_US
dc.rights	info:eu-repo/semantics/openAccess	en_US
dc.rights	Attribution 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/us/	*
dc.subject	Bladder cancer	en_US
dc.subject	Deep learning	en_US
dc.subject	Hyper-parameter optimization	en_US
dc.subject	Neural network	en_US
dc.subject	Random forest	en_US
dc.subject	Single nucleotide polymorphism	en_US
dc.title	Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer
dc.type	Article