Screening ovarian cancer by using risk factors: machine learning assists.
Biomed Eng Online
; 23(1): 18, 2024 Feb 12.
Article
in En
| MEDLINE
| ID: mdl-38347611
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 ANDMETHODS:
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Ovarian Neoplasms
/
Early Detection of Cancer
Type of study:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limits:
Female
/
Humans
Language:
En
Journal:
Biomed Eng Online
Journal subject:
ENGENHARIA BIOMEDICA
Year:
2024
Document type:
Article
Affiliation country:
Irán
Country of publication:
Reino Unido