%! Author = alex
%! Date = 3/6/25


\section{Introduction}\label{sec:introduction}
A 2024 report by McKinsey and the World Economic Forum\cite{mckinsey_health_institute_closing_2024} highlights
persistent disparities in women's healthcare and health-related research, particularly in reproductive health.
The Institute for Health Metrics and Evaluation (IHME) has identified reproductive and gynecological health issues as
the most significant factors affecting both life span and health span globally\cite{global_burden_of_disease_collaborative_network_global_2020}.
Despite their widespread impact, many aspects of reproductive health remain under-researched.
Improving our ability to understand the menstrual cycle could have significant implications for fertility tracking, contraception,
and overall reproductive health.
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The average age of pregnant women in developed countries has been increasing over the past few decades.
This combined with the overall chance of conception sharply decreasing with age, especially after 35,
makes it more and more important to understand and predict ovulation accurately\cite{sauer_reproduction_2015}.
Predicting the fertile days in a women's menstrual cycle is not only relevant for family planning but also for
natural contraception and general health monitoring, as the corresponding hormone levels have a significant impact on
the overall health and well-being of a woman.
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Ovulation is the process in which an egg is released from the ovarian follicle, making fertilization possible.
This process is regulated by hormonal changes, including fluctuations in luteinizing hormone (LH) and
follicle-stimulating hormone (FSH), and is accompanied by an increase in basal body temperature (BBT)\cite{holesh_physiology_2025}
This process is complex and yet not fully understood.
Factors such as stress, diet, and exercise can influence the menstrual cycle and make it hard to predict ovulation.
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Several physiological signs can be used to predict ovulation.
The most accurate method is ultrasonography, which detects changes in follicle size and rupture.
Other methods include detecting LH and FSH in urine, measuring BBT, and observing cervical mucus,
each with its own advantages and limitations.
These, as well as the interplay of those factors, will be discussed in more detail in Section~\ref{sec:background}.

Many studies have used these physiological signs to predict ovulation and fertility
\cite{noauthor_cervicovaginal_2005, sato_novel_2024, royston_identifying_1991, luo_detection_2020,
alexander_fertilitatsmonitoring_2014, luz_improved_2024, yu_tracking_2022, pratikno_pdf_2024}.
However, most of these methods rely on manual data collection, requiring either daily measurements or invasive procedures.
This not only makes them impractical but also results in small sample sizes, limiting their generalizability.

Generalization is crucial for developing a reliable ovulation predictor, given the high variability of the menstrual cycle
\cite{munster_length_1992, bull_real-world_2019}.
This is particularly important for applications where prediction accuracy
is critical, such as natural contraception or high-cost procedures like in-vitro fertilization (IVF),
where false predictions can have severe consequences.

This work aims to develop an ovulation predictor that is both accurate and generalizable while maintaining interpretability,
allowing for insights into key variables and patterns influencing the prediction.




% explain focus: improve explainability