Abstract
We present new results related to retrospective detection of ovulation days basing on information entered by the users of one of online platforms available in the market. Comparing to our previous studies, we improve the accuracy of algorithms which are based on evaluation and synthesis of multivariate data sources. Results are reported for 224 menstrual cycles which were labeled by medical experts. In the experiments, we pay special attention to the aspect of uncertainty associated with the tagging process.
Co-financed by the EU Smart Growth Operational Programme 2014–2020 under the project “Development of New World Scale Solutions in the Field of Machine Learning Supporting Family Planning and Overcoming the Infertility Problem”, POIR.01.01.01-00-0831/17-00.
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Fedorowicz, J. et al. (2019). Multivariate Ovulation Window Detection at OvuFriend. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_31
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DOI: https://doi.org/10.1007/978-3-030-22815-6_31
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