Project Details
Abstract
Screening of cancers has received considerable attention in developed and developing countries owing
to the heavy economic and quality-of-life burden caused by cancers. Several tools based on tumor markers
have been developed for cancer risk management. However, each tumor marker test has been developed and
validated for a specific category of anticipated cancer. In clinical practice, such tests are widely used in
monitoring response to cancer treatment, but there is a lack of evidence supporting their use for screening
multiple cancers. In our previous result, cancer screening models based on multiple serum tumor markers
were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbor
(KNN), and logistic regression, to improve the screening performance for multiple cancers in a large
asymptomatic population. In this research project, we explore to further improve the screening performance
of cancer screening models from the following aspects : (1) using other machine learning methods to build
cancer screening models, (2) integrating feature selection and machine learning methods to build cancer
screening models, and (3) using over-sampling, under-sampling as well as instance selection methods to
pre-process collected data, and then integrating feature selection and machine learning methods to build
cancer screening models.
Project IDs
Project ID:PB10703-1495
External Project ID:MOST106-2221-E182-071
External Project ID:MOST106-2221-E182-071
Status | Finished |
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Effective start/end date | 01/08/17 → 31/07/18 |
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