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Asadi, F, Salehnasab, C, Ajori, L. (1399). Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer. سامانه مدیریت نشریات علمی, 10(4), 513-522. doi: 10.31661/jbpe.v0i0.1912-1027
F Asadi; C Salehnasab; L Ajori. "Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer". سامانه مدیریت نشریات علمی, 10, 4, 1399, 513-522. doi: 10.31661/jbpe.v0i0.1912-1027
Asadi, F, Salehnasab, C, Ajori, L. (1399). 'Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer', سامانه مدیریت نشریات علمی, 10(4), pp. 513-522. doi: 10.31661/jbpe.v0i0.1912-1027
Asadi, F, Salehnasab, C, Ajori, L. Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer. سامانه مدیریت نشریات علمی, 1399; 10(4): 513-522. doi: 10.31661/jbpe.v0i0.1912-1027

Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer

Journal of Biomedical Physics and Engineering
مقاله 13، دوره 10، شماره 4، آبان 2020، صفحه 513-522    اصل مقاله (804.9 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1912-1027
نویسندگان
F Asadi* 1؛ C Salehnasab2؛ L Ajori3
1PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD Candidate, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3MD, PhD, Department of obstetrics and gynaecology, preventative gynaecology Research centre, Shahid Beheshti University of Medical Sciences Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Cervical Cancer؛ Machine Learning؛ Prediction؛ Neural networks؛ Decision Trees؛ Support Vector Machine
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