ISSN 0914-4935
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Performance Improvement of Ovarian Cancer Classification Model Using Multiple Biomarkers and Menopause Information

Hye-Jeong Song, Min-Sun Kyung, Eun-Suk Yang, Jong-Dae Kim, Chan-Young Park, and Yu-Seop Kim

(Received May 31, 2018; Accepted January 8, 2019)

Keywords: ovarian cancer, IVDMIA, marker combination, cancer classification, menopause

The tumor biomarker test used for the early diagnosis of ovarian cancer is a relatively simple test using blood. In previous studies, an optimal combination of 2 or 3 biomarkers from 16 cancer biomarkers showing specific response to ovarian cancer was designed to obtain an ovarian cancer classification model. Menopause is an important information for diagnosing ovarian cancer. In this study, we applied the menopausal status to the classification model and confirmed the performance results. The performance of the classification model is better with menopausal clinical information without such information.

Corresponding author: Min-Sun Kyung



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