Monitoring and early warning of ovarian cancer using high-dimensional non-parametric EWMA control chart based on sliding window.

ABSTRACT: Ovarian tumors are a common ovarian dysfunction that affects women's daily lives. Although ovarian tumors are generally sensitive to chemotherapy and initially respond well to platinum/taxane-based treatments, the postoperative recurrence rate remains high in advanced cases. Many researchers are dedicated to developing new methods for monitoring and predicting malignant tumors. Traditional approaches use dimensionality reduction techniques, like principal component analysis and deep learning, to select relevant features, followed by univariate or multivariate control charts for monitoring. However, these methods may overlook interactions between features and dimensionality reduction can result in loss of information, potentially affecting the accuracy of the model and leading to delayed al

SUBMITTER: Wu B 

PROVIDER: S-EPMC11911426 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature