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Artificial Intelligence in Personalized Healthcare Analysis for Womens’ Menstrual Health Disorders

Sosnowski Łukasz, Żuławińska Joanna, Dutta Soma, Szymusik Iwona, Zyguła Aleksandra, Bambul-Mazurek Elżbieta

Annals of Computer Science and Information Systems

2022

Vol. 30

751--760

The paper presents an AI-based model which depending on the input of a woman for a finite number of menstrual cycles helps in determining the possible ovulation dates as well as possibility of some health risks e.g., Premenstrual Syndrome, Luteal Phase Defect etc. The architecture of the model consists of three layers, namely analyzing and detecting the features from a single cycle, analyzing cycle level concepts based on the analyzed features, and analyzing the user's health risks based on the cycle level concepts accumulated over a finitely many cycles.