Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer.
Cervical Cancer
Decision Trees
Machine Learning
Neural networks
Prediction
Support Vector Machine
Journal
Journal of biomedical physics & engineering
ISSN: 2251-7200
Titre abrégé: J Biomed Phys Eng
Pays: Iran
ID NLM: 101589641
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
received:
16
12
2019
accepted:
05
01
2020
entrez:
18
8
2020
pubmed:
18
8
2020
medline:
18
8
2020
Statut:
epublish
Résumé
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. The present study was conducted to predict cervical cancer and identify its important predictors using machine learning classification algorithms. 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). 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. 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.
Sections du résumé
BACKGROUND
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
OBJECTIVE
The present study was conducted to predict cervical cancer and identify its important predictors using machine learning classification algorithms.
MATERIAL AND METHODS
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 32802799
doi: 10.31661/jbpe.v0i0.1912-1027
pii: JBPE-10-4
pmc: PMC7416093
doi:
Types de publication
Journal Article
Langues
eng
Pagination
513-522Informations de copyright
Copyright: © Journal of Biomedical Physics and Engineering.
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