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Analysis for Women’s’ Menstrual Health Disorders Using Artificial Intelligence

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Information Technology for Management: Approaches to Improving Business and Society (FedCSIS-AIST 2022, ISM 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 471))

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Abstract

This paper presents some developments related to a project aiming to develop an AI-based model which can determine the possible ovulation dates as well as possibility of some health risks based on the input of a woman for a finite number of menstrual cycles. In some earlier papers, the AI schemes for some health risks, such as PMS, LPD, are already discussed. In this paper, additionally the schemes for hypothyroidism and polycystic ovary syndrome (PCOS) are presented. The model is based on a ontology of medical concepts, mathematical formulations of which are designed based on the data obtained from different users over a finite number of menstrual cycles and usual relationships among different parameters determining such concepts. The mathematical formulations of the concerned medical concepts are developed by using some notions of fuzzy linguistic labels and comparators.

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Notes

  1. 1.

    www.ovufriend.pl.

  2. 2.

    https://www.womenshealth.gov/menstrual-cycle/premenstrual-syndrome.

  3. 3.

    https://www.webmd.com/infertility-and-reproduction/guide/luteal-phase-defect.

  4. 4.

    https://progyny.com/education/female-infertility/understanding-uterine-fibroids-polyps/.

  5. 5.

    In this condition the ovaries produce an abnormal amount of androgens, that are usually present in women in small amounts [9].

  6. 6.

    Hypothyroidism means the thyroid gland does not produce enough thyroid hormones, which can lead to changes in the menstrual cycle. (https://helloclue.com/articles/cycle-a-z/hypothyroidism-and-the-menstrual-cycle).

  7. 7.

    https://qz.com/2129025/where-did-ibm-go-wrong-with-watson-health.

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Acknowledgement

The research presented in this paper is co-financed by the EU Smart Growth Operational Program 2014-2020 under the project “Developing innovative solutions in the domain of detection of frequent intimate and hormonal health disorders in women of procreative age based on artificial intelligence and machine learning - OvuFriend 2.0”, POIR.01.01.01-00-0826/20.

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Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Łukasz Sosnowski

  2. University of Warmia and Mazury in Olsztyn, Słoneczna 54, 10-710, Olsztyn, Poland

    Soma Dutta

  3. Department of Obstetrics and Gynecology, Medical University of Warsaw, Żwirki i Wigury 61, 02-091, Warsaw, Poland

    Iwona Szymusik

Authors
  1. Łukasz Sosnowski
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  2. Soma Dutta
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  3. Iwona Szymusik
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Correspondence to Łukasz Sosnowski .

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Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

  2. University of Warsaw, Warsaw, Poland

    Witold Chmielarz

  3. University of Szczecin, Szczecin, Poland

    Jarosław Wątróbski

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Sosnowski, Ł., Dutta, S., Szymusik, I. (2023). Analysis for Women’s’ Menstrual Health Disorders Using Artificial Intelligence. In: Ziemba, E., Chmielarz, W., Wątróbski, J. (eds) Information Technology for Management: Approaches to Improving Business and Society. FedCSIS-AIST ISM 2022 2022. Lecture Notes in Business Information Processing, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-031-29570-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-29570-6_4

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