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: 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 ۱۴۵ patients with ۲۳ attributes, which referred to Shohada Hospital Tehran, Iran during ۲۰۱۷–۲۰۱۸, 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 ۹۵.۵۵, ۹۰.۴۸, ۱۰۰, and ۹۵.۲۰, ۹۵.۵۵, ۹۰.۴۸, ۱۰۰, and ۹۵.۲۰, those of RBF ۹۵.۴۵, ۹۰.۰۰, ۱۰۰ and ۹۱.۵۰, those of SVM ۹۳.۳۳, ۹۰.۴۸, ۹۵.۸۳ and ۹۵.۸۰ and those of MLP ۹۰.۹۰, ۹۰.۰۰, ۹۱.۶۷ and ۹۱.۵۰ 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.

Keywords:

Cervical Cancer , Machine Learning , Prediction , Neural networks , Decision Trees , Support Vector Machine