The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women
| Autor: | Ioannis Panayiotides, Abraham Pouliakis, Charalampos Chrelias, Vasileia Damaskou, Vasilios Pergialiotis, Christos Parthenis, Nicolaos Papantoniou |
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| Rok vydání: | 2018 |
| Předmět: |
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medicine.medical_specialty medicine.medical_treatment Logistic regression 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests medicine Humans Vaginal bleeding Aged Retrospective Studies 030219 obstetrics & reproductive medicine Obstetrics business.industry Endometrial cancer Decision Trees Public Health Environmental and Occupational Health Cancer Reproducibility of Results Regression analysis General Medicine Middle Aged medicine.disease Curettage Regression Endometrial Neoplasms Postmenopause 030220 oncology & carcinogenesis Case-Control Studies Regression Analysis Female Neural Networks Computer medicine.symptom business |
| Zdroj: | Public health. 164 |
| ISSN: | 1476-5616 |
| Popis: | Objective 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. Study design We conducted a retrospective case-control study based on data from analysis of pathology reports of curettage specimens in postmenopausal women. Methods Classical regression analysis was performed in addition to ANN and CART analysis using the IBM SPSS and Matlab statistical packages. Results 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%. Conclusion Artificial intelligence is a powerful mathematical tool that may significantly promote public health. It may be used as a non-invasive screening tool to guide clinicians involved in primary care decision making when endometrial pathology is suspected. |
| Databáze: | OpenAIRE |
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