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Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

Interactive, Personalized Decision Support in Analyzing Women’s Menstrual Disorders

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DOI: http://dx.doi.org/10.15439/2023F1683

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 279286 ()

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Abstract. This paper is in continuation to the paper published in FedCsis 2022. In the earlier paper we presented the general scheme behind the AI based model for determining the possible ovulation dates as well as the possibility of some health risks. Here apart from the already discussed schemes for Premenstrual Syndrome (PMS), Luteal Phase Defect (LPD), and polyp and fibroids, a few additional schemes like hypothyroidism, polycystic ovary syndrom (PCOS) are included. Moreover, we attempt to throw light on the novelty of this AI based scheme from the perspective personalized, case sensitive, interactive medical support which does not depend only on a preset rule based system for diagnosing diseases.


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