Screening ovarian cancer by using risk factors: machine learning assists

Abstract

Background and aim Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes. Materials and methods As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC). Results Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91–0.95]) was recognized as the best-performing model for predicting OC. Conclusions ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.

Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

“…Furthermore, logistic regression selects features having a statistically significant hybrid correlation with the output class. The combination of logistic regression as a multi-variable selection strategy for feature selection and ML algorithms has a substantial role in enhancing the performance efficiency of these algorithms, and this subject has been shown in previous studies on biomedical research [ 32 – 34 ].…”

“…The base algorithms also included Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM). These algorithms were selected based on their popularity in high-performing capability for prediction purposes and their extensive use in studies on healthcare topics [ 34 – 36 ].…”