F Asadi, C Salehnasab, L Ajori
PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
PhD Candidate, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Biomed Phys Eng. 2020 Aug 1;10(4):513-522. doi: 10.31661/jbpe.v0i0.1912-1027. eCollection 2020 Aug.
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.
与其他妇科癌症相比，宫颈癌在第三世界国家的女性中最为普遍，也是主要的死亡原因，受吸烟、营养状况差、免疫缺陷、长期使用避孕药等不同因素影响。
本研究旨在使用机器学习分类算法预测宫颈癌并确定其重要预测因素。
在一项横断面研究中，对2017年至2018年期间转诊至伊朗德黑兰烈士医院的145例具有23个属性的患者数据，采用支持向量机（SVM）、QUEST、C&R树、多层感知器（MLP）和径向基函数（RBF）等机器学习分类算法进行分析。用于评估这些算法的标准测量指标包括准确率、灵敏度、特异性和曲线下面积（AUC）。
QUEST和C&R树的准确率、灵敏度、特异性和AUC分别为95.55、90.48、100和95.20，RBF的分别为95.45、90.00、100和91.50，SVM的分别为93.33、90.48、95.83和95.80，MLP的分别为90.90、90.00、91.67和91.50百分比。发现所有算法中的重要预测因素包括个人健康水平、婚姻状况、社会地位、避孕药使用剂量、教育程度和剖宫产次数。
本研究证实机器学习可以提高宫颈癌的预测能力。本研究结果表明，决策树算法可用于识别最相关的预测因素。此外，改善患者的个人健康和社会文化水平似乎有助于预防宫颈癌。
