TY - JOUR T1 - Machine learning model to identify prognostic factors in glioblastoma: a SEER-based analysis TT - Glioblastomda prognostik faktörleri tanımlamak için makine öğrenmesi modeli: SEER tabanlı analiz AU - Yakar, Fatih AU - Bakırarar, Batuhan AU - Egemen, Emrah AU - Dere, Ümit Akın PY - 2023 DA - April Y2 - 2023 DO - 10.31362/patd.1179139 JF - Pamukkale Medical Journal JO - Pam Tıp Derg PB - Pamukkale Üniversitesi WT - DergiPark SN - 1308-0865 SP - 338 EP - 348 VL - 16 IS - 2 LA - en AB - Purpose: Analyzing and interpreting large amounts of complex health care data are becoming more insufficientby traditional statistical approaches. However, analyzing Big Data (BD) by machine learning (ML) supports thestorage, classification of patient information. Therefore, improves disease identification, treatment evaluation,surgical planning, and outcome prediction. The current study aims to create a competing risk model to identifyprognostic factors in glioblastoma (GB).Materials and methods: The study included 31663 patients diagnosed with GB between 2007 and 2018. Thedata in the study were taken from the Surveillance, Epidemiology, and End Results (SEER) database. Overallsurvivals (OS), age, race, gender, primary site, laterality, surgery and tumor size at the time of diagnosis, vitalstatus, and follow-up time (months) were selected for the analyzes.Results: The median OS of the patients was found to be 9.00±0.09 months. In addition, all variables in thetable were statistically significant risk factors for survival except gender. Therefore, surgery, age, laterality,primary site, tumor size, race, gender variables were used as independent risk factors, and vital status wasused as a dependent variable for ML analysis. Looking at the ML results, hybrid model gave the best resultsaccording to Accuracy, F-measure, and MCC performance criteria. According to hybrid model, which has thebest performance, the diagnosis of alive/dead in 84 and 74 out of 100 patients can be interpreted as correct for1- and 2-year, respectively.Conclusions: The model created by ML was 84.9% and 74.1% successful in predicting 1- and 2-year survivalin GB patients, respectively. Recognition of the fundamental ideas will allow neurosurgeons to understand BDand help evaluate the extraordinary amount of data within the associated healthcare field. KW - Machine learning KW - big data KW - glioblastoma KW - SEER N2 - Amaç: Büyük miktarlardaki karmaşık sağlık hizmeti verilerinin analiz edilmesi ve yorumlanmasında gelenekselistatistiksel yaklaşımlar giderek yetersiz kalmaktadır. Bununla birlikte, Büyük Verinin makine öğrenmesi ile analizedilmesi, hasta bilgilerinin depolanmasını, sınıflandırılmasını destekler. Bu nedenle hastalık tanımlamasını,tedavi değerlendirmesini, cerrahi planlamayı ve sonuç tahminini geliştirir. Mevcut çalışma, glioblastomda (GB)prognostik faktörleri tanımlamak için bir risk modeli oluşturmayı amaçlamaktadır.Gereç ve yöntem: Çalışmaya 2007-2018 yılları arasında GB tanısı konan 31663 hasta dahil edilmiştir.Çalışmadaki veriler Surveillance, Epidemiology, and End Results (SEER) veri tabanından alınmıştır. Analizleriçin genel sağ kalımlar, yaş, ırk, cinsiyet, primer bölge, lateralite, cerrahi ve tanı anındaki tümör boyutu, vitaldurum ve takip süresi (ay) seçildi.Bulgular: Hastaların ortanca sağ kalımı 9,00±0,09 ay olarak bulundu. Ayrıca tablodaki tüm değişkenler cinsiyetdışında sağ kalım için istatistiksel olarak anlamlı risk faktörleriydi. Bu nedenle, makine öğrenmesi analizi içinbağımsız risk faktörleri olarak cerrahi, yaş, lateralite, primer bölge, tümör boyutu, ırk, cinsiyet değişkenleri vevital durum bağımlı değişken olarak kullanıldı. Makine öğrenmesi sonuçlarına bakıldığında, doğruluk, F-ölçümüve MCC performans kriterlerine göre Hibrit Model en iyi sonuçları vermiştir. En iyi performansa sahip olan hibritmodele göre 100 hastanın 84'ünde canlı/ölü tanısı sırasıyla 1 ve 2 yıl için doğru olarak yorumlanabilmektedir.Sonuç: Makine öğrenmesi ile oluşturulan model GB hastalarında 1 ve 2 yıllık sağ kalımı öngörmede sırasıyla%84,9 ve %74,1 başarılıydı. Temel fikirlerin tanınması, beyin cerrahlarının Büyük Veriyi anlamalarına ve ilgilisağlık hizmetleri alanındaki olağanüstü miktarda veriyi değerlendirmelerine yardımcı olacaktır. CR - 1. Yakar F, Egemen E, Çeltikçi E, et al. The big data awareness of Turkish neurosurgeons: a national survey. J Nervous Sys Surgery 2022;8:9-16. https://doi.org/10.54306/SSCD.2022.200 CR - 2. Hinton GE, Osindero S, Teh YW. A fast-learning algorithm for deep belief nets. Neural Comput 2006;1:1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527 CR - 3. White SE. A review of big data in healthcare: challenges and opportunities. Open Access Bioinf 2014;6:13-18. https://doi.org/10.2147/OAB.S50519 CR - 4. 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