Monitoring and early warning of ovarian cancer using high-dimensional non-parametric EWMA control chart based on sliding window
Bin Wu, Wen Zhong, Yixing Ren, Zhongli Zhou, Liu Liu
TL;DR
This paper introduces a new statistical method for monitoring ovarian cancer recurrence using high-dimensional data without losing information through dimensionality reduction.
Contribution
A novel sliding window EWMA control chart is proposed for high-dimensional data, avoiding dimensionality reduction and improving early warning accuracy.
Findings
The new method outperforms traditional dimensionality-reduced approaches in detecting changes more rapidly.
Empirical validation on tumor resection data confirms the effectiveness of the proposed control chart.
The method is suitable for high-dimensional data with unknown distributions.
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 alerts and reduced predictive performance. Therefore, this paper develops a new sliding window EWMA…
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Taxonomy
TopicsAdvanced Statistical Process Monitoring · Statistical Methods and Inference · Advanced Statistical Methods and Models