Alex Blank
67 lines
4.7 KiB
TeX
67 lines
4.7 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Introduction}\label{sec:introduction}
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In textbooks, a menstrual cycle is 28 to 30 days in length with its ovulation happening around day 14~\cite{Phy}
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The average age of pregnant women in developed countries has been increasing over the past few decades.
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Combined with the sharp decline in conception rates after age 35,
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this trend underscores the growing need for accurate understanding of the menstrual cycle~\cite{sauer_reproduction_2015}.
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Predicting the fertile days in a women's menstrual cycle is not only relevant for family planning but also for
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natural contraception and general health monitoring, as the corresponding hormone levels have a significant impact on
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the overall health and well-being of a woman.
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Textbook cycles usually have a length of 28 days with an ovulation around day 14.
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This however, does not represent the real world variability of menstrual cycles.
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Ovulation is the process in which an egg cell is released from the ovaries, making fertilization possible.
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This process is regulated by hormonal changes, including fluctuations in luteinizing hormone (LH) and
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follicle-stimulating hormone (FSH), and is accompanied by other physiological changes such as an increase in electrical resistance
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and viscosity of the cervical mucus or an increase in body temperature~\cite{wallach_prediction_1980}.
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These processes remain incompletely understood and are influenced by lifestyle factors such as stress, diet or exercise or
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health-related factors such as Polycystic Ovary Syndrome (PCOS), making ovulation difficult to predict.
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In theory, these physiological changes provide a basis for predicting ovulation and the surrounding fertile window.
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In practice, however, many of these signals are difficult to measure continuously, as they require invasive procedures,
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manual tracking, or costly equipment.
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Body temperature, by contrast, is a non-invasive marker that can be measured continuously using intravaginal, wrist-worn, or ear-worn thermometers.
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While some studies have deemed body temperature unusable for predictive analysis~\cite{bauman_basal_1981},
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others suggest its predictive value for ovulation~\cite{sato_novel_2024, royston_identifying_1991,
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luo_detection_2020, alexander_fertilitatsmonitoring_2014, luz_improved_2024, yu_tracking_2022, pratikno_pdf_2024}.
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However, most existing studies rely on small, idealized datasets that exclude cycles with irregular lengths or late ovulation.
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While such restrictions simplify the prediction task and yield high accuracy, they give a misleading impression of real-world model performance.
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It is thus not yet fully clear, whether temperature can reliably be used as a predictive marker for ovulation or fertility.
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Additionally, other studies have focused on peripheral temperature measurements of skin or in-ear temperature,
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which are subject to many sources of noise that can significantly affect the quality of the resulting predictions.
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Intravaginal temperature reflects true core body temperature and offers higher resolution and stability,
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as it is largely unaffected by external circumstances.
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This allows for more reliable detection of subtle thermal shifts associated with ovulation, especially in irregular cycles.
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Generalization is critical for reliable ovulation prediction,
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especially given the high variability in cycle patterns~\cite{munster_length_1992, bull_real-world_2019}.
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This is particularly relevant in high-stakes applications such as natural contraception or in-vitro fertilization (IVF),
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where inaccurate predictions can have serious consequences.
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This study aims to advance ovulation prediction by leveraging an extensive database of more than 40,000 menstrual cycles recorded using
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an intravaginal wearable device that continuously measures core body temperature.
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The objective is to develop a machine learning model that performs reliably across diverse cycle types, including irregular ones.
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To this end, we compare a set of time series-based machine learning architectures and evaluate their performance for
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natural family planning and contraception.
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Finally, we demonstrate that high predictive accuracy on highly regular, curated datasets, as commonly reported in prior work,
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does not reflect general applicability, since such datasets tend to favor even simple, rule-based approaches.
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The research objectives are:
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\begin{itemize}
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\item To assess the performance of different model architectures on the use cases of natural family planning and contraception, across both regular and irregular cycles.
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\item To compare sophisticated machine learning models with simple rule-based baseline approaches.
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\item To study the overall predictive quality of temperature for ovulation and fertility prediction.
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\end{itemize}
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