%! 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


\section{Future Work}\label{sec:future_work}

% inclusion of markers
% better labeling process
% more advanced models
%