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


\section{Discussion}\label{sec:discussion}

In this study, we investigated the performance of different machine learning architectures on the task of fertility prediction,
with the aim to find a model that performs well for natural family planning and natural contraception on regular and irregular cycles.

Our goal was to

Based on an extensive real-world database and established model architectures for timeseries analysis,
we expect to outperform both rule-based baselines and related studies.
We think, that for regular cycles, the performance difference will be lower than


% talk about whether bbt / temperature can be used for such a task, discuss bbt doubt papers
% While previous work has argued against the predictive value of BBT~\cite{some_author_2010}, our findings suggest otherwise.
%Using continuous core body temperature data from 40,000 cycles, we demonstrate that temperature-based models can reliably detect ovulatory patterns, even in the presence of physiological noise or mild irregularity.
% explain the need for further medical interpretation of the results of either model


The findings do not show a clear indication, that the temperature can be used as a predictive target.
The results of irregular cycles should be significantly better to infer, that there are usable patters in the
temperature readings before the ovulation or the fertile days.

For a practical use case of any model's prediction, a medical interpretation should be performed.
While the results themselves can give a clear indication of both the fertility probability and whether the ovulation
of a given cycle is already over for any given day, there a variety of external factors that should be taken into
consideration for a direct output to the user.



\section{Future Work}\label{sec:future_work}
There are several directions in which this study could be extended,
most of which were omitted due to time and resource constraints but represent valuable areas for future exploration.

One major area is feature selection.
The dataset used includes additional user-entered markers such as physiological signs (e.g., bleeding, illness, stress)
and external events (e.g., intercourse, pregnancy tests).
These markers were not included in the present analysis but may carry predictive value and could meaningfully improve model performance.
Similarly, the introduction of engineered or intermediate features—derived from raw inputs—may help models better
capture relevant patterns and temporal dependencies.

Alternative target formulations could also be explored to better reflect the structure of the fertile window and ovulation.
For example, instead of predicting a daily fertility probability, models could aim to identify the absolute day of ovulation,
or estimate the time until the next (or since the last) ovulation event.

As noted in Section~\ref{subsubsec:data_labeling}, ovulation labels were assigned using a
retrospective algorithm trained on expert-annotated data.
Any inaccuracies in this algorithm propagate directly to the supervised learning labels.
Thus, improving prediction quality may require a newly labeled dataset—ideally combining expert review with algorithmic assistance.

Finally, while the models used in this study (LSTMs and Transformers) are well-established for time-series analysis,
they were not extensively customized.
Future work could involve tailoring architectures more specifically to the characteristics of menstrual cycle data.
Recent transformer variants designed for time series, such as \emph{TimeXer}~\cite{wang_timexer_2024} or \emph{MAMBA}~\cite{wang_is_2024},
may offer improved performance.
Alternatively, a custom architecture could be developed to better reflect the domain-specific structure of biological temperature data.

Future work may incorporate more advanced hyperparameter optimization techniques,
such as Bayesian Optimization, Genetic Algorithms, or Neural Architecture Search (NAS),
to better explore the joint parameter space in a more efficient and principled manner.