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

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


\section{Methodology}\label{sec:methodology}

% why did I select tft over other methods -> include examples of time series and why I belief a complex model could help
% you did I apply it
% implementation details

\subsection{Data Collection \& Preprocessing}\label{subsec:data_collection_preprocessing}

\subsubsection{Data Collection}\label{subsubsec:data_collection}
The dataset used in this work was collected by \textit{VivoSensMedical GmbH}, a medical technology company based in Leipzig, Germany,
specializing in fertility tracking devices and applications.
Each entry in the dataset is derived from temperature measurements recorded by the \textit{OvulaRing} wearable
device\cite{alexander_fertilitatsmonitoring_2014}, which continuously measures core body temperature every 5 minutes.
The sensor is worn intra-vaginally and is designed for extended use, requiring removal only for data synchronization.
This enables high-resolution, long-term temperature tracking, providing a detailed view of menstrual cycle patterns.

% add a few example cycles
% TODO: stats about dataset
The dataset used for training and evaluation consists of 45000 cycles,

In addition to raw temperature values, the dataset includes user-specific metadata, such as health status, weight, and age.
Users can also manually input time-dependent markers, indicating events such as menstruation, ovulation, and intercourse,
which serve as contextual features for the model.
While these additional data points can enhance predictive accuracy, it is important to note that they are self-reported
and may be subject to errors or biases.

\subsubsection{Data Preprocessing}\label{subsubsec:data_preprocessing}
Not all cycles in the dataset can be used for training.
Cycles that are too short are excluded, as they usually do not contain enough information to make reliable predictions.
Additionally, cycles that are too long are also excluded, as they might contain multiple ovulation events or
pregnancies, which would make the prediction task ambiguous.

\subsubsection{Feature Engineering}\label{subsubsec:feature_engineering}

%TODO: show that a model without marker context and one with them is trained, to show the impact of the markers

%TODO: show that for initial testing and fine tuning the dataset was reduced to 10% of size to speed up early impressions of performance
%TODO: show that initial feature set with only temperature as observable and only pregnancy "chance" as target
% did not show promise on small 10% dataset, so the features got extended
% show the modifications that were made to account for multi target prediction
% show, the mothod of circumventing the non-padding-implementation by using a dedicated feature
% show, that a different loss / temporal loss wheighting or position aware loss could help
% show, the problem of the model using its past targets, even though they are not observable and how I dealt with this -> explain and use exogenous
% introduce change of quantiles to single variable output and why, quantiles loss other than .5 was almost always just 0 or 1, so no significant predicitve quality in this, and this only makes predictions harder for model
% explain why class imablances are there, and what I did to counteract this, also explain, why special weighting is useful for something like the pregnancy risk prediction
% show the work that needed to be done for inference -> padding values for future observables

\subsubsection{Data Labeling}\label{subsubsec:data_labeling}

Supervised machine learning requires labeled data—i.e., known target values for each input.
In the context of this study, the relevant target is the ovulation day within each menstrual cycle.

The dataset contains over 40{,}000 cycles, making manual annotation unfeasible.
Instead, a retrospective ovulation detection algorithm is employed to assign labels:
(1) whether a cycle is ovulatory or anovulatory, and
(2) if ovulatory, the estimated day of ovulation.

This labeling algorithm was developed in collaboration with a gynecologist and reproductive medicine specialist.
It is trained on a curated reference set of cycles with expert-assigned labels based on domain knowledge and characteristic temperature patterns.

The algorithm operates in two stages:
\begin{enumerate}
    \item \textbf{Cycle classification:} Each cycle is classified as either monophasic (anovulatory) or biphasic (ovulatory), based on the presence of a luteal-phase temperature shift.
    \item \textbf{Ovulation estimation:} For biphasic cycles, the most likely day of ovulation is identified retrospectively using the full temperature curve.
\end{enumerate}

This retrospective labeling provides a practical and scalable proxy for ground truth, enabling training and evaluation across a large, real-world dataset,
especially, as temperature is at least an excellent retrospective marker for ovulation.
%TODO: provide statistics for the retrospective algorithm

These labels are then used as ground truth labels for the prediction algorithms.
We are aware of potential shortcomings with this approach.
We acknowledge that this approach introduces some label noise—particularly in edge cases where temperature signals are
ambiguous or distorted (e.g., illness, missed measurements).
These inaccuracies may propagate into the predictive models.
However, label quality is continuously reviewed and can be refined as part of future model iterations.

The usage of the ovulation day labels in the model input features is documented in section~\ref{subsubsec:input_output_modeling}


% show, that for very regular cycles, no sophisticated methods are necessary

\subsection{Time-Series Modeling Approach}\label{subsec:time-series_modeling_approach}

\subsubsection{Input \& Output Modeling}\label{subsubsec:input_output_modeling}

% build in the probability curve for the pregnancy chance

\subsection{Model Training}\label{subsec:model_training}

\subsubsection{Training Setup}\label{subsubsec:training_setup}

\subsubsection{Hyperparameter Tuning}\label{subsubsec:hyperparameter_tuning}

\subsubsection{Training Details}\label{subsubsec:training_details}

\subsection{Evaluation}\label{subsec:evaluation}

\subsubsection{Evaluation Metrics}\label{subsubsec:evaluation_metrics}

\subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}