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Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer
BACKGROUND: Compared to other genital cancers, cervical cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. OBJECTIVE...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Shiraz University of Medical Sciences
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416093/ https://www.ncbi.nlm.nih.gov/pubmed/32802799 http://dx.doi.org/10.31661/jbpe.v0i0.1912-1027 |
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author | F., Asadi C., Salehnasab L., Ajori |
author_facet | F., Asadi C., Salehnasab L., Ajori |
author_sort | F., Asadi |
collection | PubMed |
description | BACKGROUND: Compared to other genital cancers, cervical cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. OBJECTIVE: The present study was conducted to predict cervical cancer and identify its important predictors using machine learning classification algorithms. MATERIAL AND METHODS: In a cross-sectional study, the data of 145 patients with 23 attributes, which referred to Shohada Hospital Tehran, Iran during 2017–2018, were analyzed by machine learning classification algorithms which included SVM, QUEST, C&R tree, MLP and RBF. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, specificity and area under the curve (AUC). RESULTS: The accuracy, sensitivity, specificity and AUC of Quest and C&R tree were, respectively 95.55, 90.48, 100, and 95.20, 95.55, 90.48, 100, and 95.20, those of RBF 95.45, 90.00, 100 and 91.50, those of SVM 93.33, 90.48, 95.83 and 95.80 and those of MLP 90.90, 90.00, 91.67 and 91.50 percentage. The important predictors in all the algorithms were found to comprise personal health level, marital status, social status, the dose of contraceptives used, level of education and number of caesarean deliveries. CONCLUSION: This investigation confirmed that ML can enhance the prediction of cervical cancer. The results of this study showed that Decision Tree algorithms can be applied to identify the most relevant predictors. Moreover, it seems that improving personal health and socio-cultural level of patients can be causing cervical cancer prevention. |
format | Online Article Text |
id | pubmed-7416093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Shiraz University of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-74160932020-08-14 Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer F., Asadi C., Salehnasab L., Ajori J Biomed Phys Eng Original Article BACKGROUND: Compared to other genital cancers, cervical cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. OBJECTIVE: The present study was conducted to predict cervical cancer and identify its important predictors using machine learning classification algorithms. MATERIAL AND METHODS: In a cross-sectional study, the data of 145 patients with 23 attributes, which referred to Shohada Hospital Tehran, Iran during 2017–2018, were analyzed by machine learning classification algorithms which included SVM, QUEST, C&R tree, MLP and RBF. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, specificity and area under the curve (AUC). RESULTS: The accuracy, sensitivity, specificity and AUC of Quest and C&R tree were, respectively 95.55, 90.48, 100, and 95.20, 95.55, 90.48, 100, and 95.20, those of RBF 95.45, 90.00, 100 and 91.50, those of SVM 93.33, 90.48, 95.83 and 95.80 and those of MLP 90.90, 90.00, 91.67 and 91.50 percentage. The important predictors in all the algorithms were found to comprise personal health level, marital status, social status, the dose of contraceptives used, level of education and number of caesarean deliveries. CONCLUSION: This investigation confirmed that ML can enhance the prediction of cervical cancer. The results of this study showed that Decision Tree algorithms can be applied to identify the most relevant predictors. Moreover, it seems that improving personal health and socio-cultural level of patients can be causing cervical cancer prevention. Shiraz University of Medical Sciences 2020-08-01 /pmc/articles/PMC7416093/ /pubmed/32802799 http://dx.doi.org/10.31661/jbpe.v0i0.1912-1027 Text en Copyright: © Journal of Biomedical Physics and Engineering http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 Unported License, ( http://creativecommons.org/licenses/by-nc/4.0/ ) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article F., Asadi C., Salehnasab L., Ajori Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer |
title | Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer |
title_full | Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer |
title_fullStr | Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer |
title_full_unstemmed | Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer |
title_short | Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer |
title_sort | supervised algorithms of machine learning for the prediction of cervical cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416093/ https://www.ncbi.nlm.nih.gov/pubmed/32802799 http://dx.doi.org/10.31661/jbpe.v0i0.1912-1027 |
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