Analysis for Women’s’ Menstrual Health Disorders Using Artificial Intelligence
Lecture Notes in Business Information Processing, ISSN: 1865-1356, Vol: 471 LNBIP, Page: 71-90
2023
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures7
- Readers7
Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85153118162&origin=inward; http://dx.doi.org/10.1007/978-3-031-29570-6_4; https://link.springer.com/10.1007/978-3-031-29570-6_4; https://dx.doi.org/10.1007/978-3-031-29570-6_4; https://link.springer.com/chapter/10.1007/978-3-031-29570-6_4
Springer Science and Business Media LLC
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