Optimization of Cervical Cancer Screening: A Stacking-Integrated Machine Learning Algorithm Based on Demographic, Behavioral, and Clinical Factors.

ABSTRACT:

Purpose

The purpose is to accurately identify women at high risk of developing cervical cancer so as to optimize cervical screening strategies and make better use of medical resources. However, the predictive models currently in use require clinical physiological and biochemical indicators, resulting in a smaller scope of application. Stacking-integrated machine learning (SIML) is an advanced machine learning technique that combined multiple learning algorithms to improve predictive performance. This study aimed to develop a stacking-integrated model that can be used to identify women at high risk of developing cervical cancer based on their demographic, behavioral, and historical clinical factors.

Methods

The data of 858 women screened for cervical cancer at a Venezuelan Ho

SUBMITTER: Sun L 

PROVIDER: S-EPMC8886038 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature