Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

ABSTRACT: One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student's t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Mach

SUBMITTER: Ahamad MM 

PROVIDER: S-EPMC9394434 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature