temperature-based-fertility-prediction-thesis/thesis/sections/introduction.tex

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


\section{Introduction}\label{sec:introduction}

While textbooks often describe a menstrual cycle as lasting 28 to 30 days with ovulation around day 14~\cite{owen_physiology_nodate},
such regularity is the exception rather than the rule~\cite{munster_length_1992, bull_real-world_2019}.
For individuals with consistent cycle patterns, simple calendar-based predictions may suffice.
However, for the majority, especially with increasing age and associated irregularity, more sophisticated methods are necessary.
This, combined with an ever higher age of pregnancy in industrialized and industrializing countries,
underscores the growing need for accurate understanding of the menstrual cycle~\cite{sauer_reproduction_2015}.

For many women, the practical use cases of menstrual cycle monitoring are \emph{Natural Family Planning} (NFP) and contraception~\cite{earle_use_2021}.
For these use cases, it is essential to identify the ovulation and its corresponding fertile and infertile days in a cycle,
to either avoid or achieve pregnancy more effectively.

Ovulation is the process in which an egg cell is released from the ovaries, 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 other physiological changes such as an increase in electrical resistance
and viscosity of the cervical mucus or an increase in body temperature~\cite{wallach_prediction_1980}.
These processes remain incompletely understood and are influenced by lifestyle factors such as stress, diet, exercise,
or health-related conditions such as Polycystic Ovary Syndrome (PCOS), making ovulation difficult to predict.

In theory, these physiological changes provide a basis for predicting ovulation and the surrounding fertile window.
In practice, however, many of these signals are difficult to measure continuously, as they require invasive procedures,
manual tracking, or costly equipment.
Body temperature, by contrast, is a non-invasive marker that can be measured continuously using intravaginal, wrist-worn, or ear-worn thermometers.
While some studies have deemed body temperature unusable for predictive analysis~\cite{bauman_basal_1981},
others suggest its predictive value for ovulation~\cite{sato_novel_2024, royston_identifying_1991,
    luo_detection_2020, alexander_fertilitatsmonitoring_2014, luz_improved_2024, yu_tracking_2022, pratikno_pdf_2024}.

However, most existing studies rely on small, idealized datasets that exclude cycles with irregular lengths or late ovulation.
While such restrictions simplify the prediction task and yield high accuracy, they give a misleading impression of real-world model performance.
It is thus not yet fully clear whether temperature can reliably be used as a predictive marker for ovulation or fertility.

Additionally, other studies have focused on peripheral temperature measurements of skin or in-ear temperature,
which are subject to many sources of noise that can significantly affect the quality of the resulting predictions.
Intravaginal temperature reflects true core body temperature and offers higher resolution and stability,
as it is largely unaffected by external circumstances.
This allows for more reliable detection of subtle thermal shifts associated with ovulation, especially in irregular cycles.

This study aims to advance fertility prediction by leveraging an extensive database of more than 40,000 menstrual cycles,
covering cycle lengths 11 to 149 days and ovulation days ranging from 7 to 136, recorded using
an intravaginal wearable device that continuously measures core body temperature at a resolution of 288 measurements per day.
The objective is to develop a machine learning model that performs reliably across diverse cycle types, including irregular ones.
To this end, we compare a set of time series-based machine learning architectures and evaluate their performance for
NFP and contraception.
Finally, we demonstrate that high predictive accuracy on highly regular, curated datasets, as commonly reported in prior work,
may overestimate real-world applicability, since such datasets tend to favor even simple, rule-based approaches.


The research objectives are:
\begin{itemize}
    \item To evaluate the predictive value of body temperature for ovulation and fertility across diverse menstrual cycle types.
    \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.
    \item To compare sophisticated machine learning models with simple rule-based baseline approaches.
\end{itemize}