Determine the biological age or biological age group using raw ECG data

The significance of developing female-related services and businesses cannot be understated. These endeavors not only empower individuals but also contribute to the overarching goals of societal progress and gender equality. By addressing the unique and specific needs of women, we take a substantial step toward fostering a more equitable, inclusive, and health-conscious world.

In this technical hackathon challenge, participants will be tasked with developing a predictive model for estimating the time intervals between consecutive women’s menstrual cycles. The primary goal is to create a predictive framework that can accurately forecast the onset of menstruation. This predictive capability holds immense potential, offering women a valuable tool for health management, family planning, and career decisions. Participants are encouraged to explore a multitude of creative and innovative solutions, leveraging AI-driven algorithms, regression models, Bayesian statistical methodologies, or any other data-driven approaches. The culmination of this challenge may eventually see its integration into period tracking application, delivering a more precise and convenient solution for women as they navigate their menstrual health. Your participation in this hackathon stands as an opportunity to drive substantial progress in the realm of women’s health and technology, ultimately contributing to a more informed and inclusive society.

Expert Dr. Ivan Seleznov

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