The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women

Public Health
V PergialiotisI Panayiotides

Abstract

Artificial neural networks (ANNs) and classification and regression trees (CARTs) have been previously used for the prediction of cancer in several fields. In our study, we aim to investigate the diagnostic accuracy of three different methodologies (i.e. logistic regression, ANNs and CARTs) for the prediction of endometrial cancer in postmenopausal women with vaginal bleeding or endometrial thickness ≥5 mm, as determined by ultrasound examination. We conducted a retrospective case-control study based on data from analysis of pathology reports of curettage specimens in postmenopausal women. Classical regression analysis was performed in addition to ANN and CART analysis using the IBM SPSS and Matlab statistical packages. Overall, 178 women were enrolled. Among them, 106 women were diagnosed with carcinoma, whereas the remaining 72 women had normal histology in the final specimen. ANN analysis seems to perform better with a sensitivity of 86.8%, specificity of 83.3%, and overall accuracy (OA) of 85.4%. CART analysis did not perform well with a sensitivity of 78.3%, specificity of 76.4%, and OA of 77.5%. Regression analysis had a poorer predictive accuracy with a sensitivity of 76.4%, a specificity of 66.7%, and an OA of 72.5%. Ar...Continue Reading

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Citations

Mar 10, 2020·Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco·Shelley D GoldenPratima Musburger
Apr 28, 2020·Supportive Care in Cancer : Official Journal of the Multinational Association of Supportive Care in Cancer·Xin-Yi XuLi Chen
Dec 29, 2020·Cancer Management and Research·Jie ZhouLi Li
Apr 20, 2021·European Journal of Obstetrics, Gynecology, and Reproductive Biology·Stefano GuerrieroJuan Luis Alcazar
May 12, 2021·The Journal of Obstetrics and Gynaecology Research·Kenbun SoneYutaka Osuga

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