Deep Learning for Predicting Late-Onset Breast Cancer Metastasis: The Single-Hyperparameter Grid Search (SHGS) Strategy for Meta-Tuning a Deep Feed-Forward Neural Network.

ABSTRACT: Background: While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedicated to constructing a deep feed-forward neural network (DFNN) model to predict breast cancer metastasis n years in advance. However, the challenge lies in efficiently identifying optimal hyperparameter values through grid search, given the constraints of time and resources. Issues such as the infinite possibilities for continuous hyperparameters like L1 and L2, as well as the time-consuming and costly process, further complicate the task. Methods: To address these challenges, we developed the Single-Hyperparameter Grid Search (SHGS) strategy, serving as a preselection method before gri

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC12650627 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature