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.

Keywords: Machine learning; Ovarian cancer; Predictive efficiency; Preventive strategy; Public health challenge.

Fig. 1

The preprocessing steps of the samples in the dataset

Fig. 2

The ROC of ML-trained algorithms

Fig. 3

The RI of factors associated with OC prediction

Fig. 4

The importance of factors based on permutation feature score

Fig. 5

The mean SHAP values associated predictors of OC

Fig. 6

SHAP values associated with OC prediction pertaining to all cases

Fig. 7

The external capability classification

Fig. 8

The performance indicators of XG-Boost in two external settings

Fig. 9

The internal and external ROC of the XG-Boost model