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Identification of women with high grade histopathology results after conisation by artificial neural networksMlinarič, Marko, medicina ...Background: The aim of the study was to evaluate if artificial neural networks can predict high-grade histopathology results after conisation from risk factors and their combinations in patients ... undergoing conisation because of pathological changes on uterine cervix. Patients and methods: We analysed 1475 patients who had conisation surgery at the University Clinic for Gynaecology and Obstetrics of University Clinical Centre Maribor from 1993-2005. The database in different datasets was arranged to deal with unbalance data and enhance classification performance. Weka open-source software was used for analysis with artificial neural networks. Last Papanicolaou smear (PAP) and risk factors for development of cervical dysplasia and carcinoma were used as input and high-grade dysplasia Yes/No as output result. 10-fold cross validation was used for defining training and holdout set for analysis. Results: Baseline classification and multiple runs of artificial neural network on various risk factors settings were performed. We achieved 84.19% correct classifications, area under the curve 0.87, kappa 0.64, F-measure 0.884 and Matthews correlation coefficient (MCC) 0.640 in model, where baseline prediction was 69.79%. Conclusions: With artificial neural networks we were able to identify more patients who developed high-grade squamous intraepithelial lesion on final histopathology result of conisation as with baseline prediction. But, characteristics of 1475 patients who had conisation in years 1993-2005 at the University Clinical Centre Maribor did not allow reliable prediction with artificial neural networks for every-day clinical practice.Vir: Radiology and oncology. - ISSN 1318-2099 (Vol. 56, iss. 3, 2022, str. 355-364)Vrsta gradiva - članek, sestavni delLeto - 2022Jezik - angleškiCOBISS.SI-ID - 115112451
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Ime baze podatkov | Področje | Leto |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Mlinarič, Marko, medicina | |
Križmarić, Miljenko | 25641 |
Takač, Iztok | 01324 |
Repše-Fokter, Alenka | 23551 |
Vir: Osebne bibliografije
in: SICRIS
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