A feasibility study on utilizing machine learning technology to reduce the costs of gastric cancer screening in Taizhou, China.

ABSTRACT:

Aim

To optimize gastric cancer screening score and reduce screening costs using machine learning models.

Methods

This study included 228,634 patients from the Taizhou Gastric Cancer Screening Program. We used three machine learning models to optimize Li's gastric cancer screening score: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL). The performance of the binary classification models was evaluated using the area under the curve (AUC) and area under the precision-recall curve (AUCPR).

Results

In the binary classification model used to distinguish low-risk and moderate- to high-risk patients, the AUC in the GBM, DRF, and DL full models were 0.9994, 0.9982, and 0.9974, respectively, and the AUCPR was 0.9982, 0.9949, and 0.9918,

SUBMITTER: Yan SY 

PROVIDER: S-EPMC11378168 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature