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


\section{Conclusion}\label{sec:conclusion}

This thesis presented a systematic investigation of different machine learning architectures
for fertility prediction based on high-resolution body core temperature data.
By comparing LSTM- and Transformer-based models, as well as their convolutional variants,
the results show that machine learning can achieve high predictive performance.
LSTM models performed best according to standard evaluation metrics, whereas Transformer-based models
proved more robust in simulated use case evaluations for contraception and Natural Family Planning (NFP).
Predictions were consistently more reliable in regular cycles than in irregular ones,
highlighting both the potential and the inherent limits of temperature-based approaches.

A characteristic pre-ovulatory temperature drop was identified as correlating with fertility.
Both its timing and its magnitude appear to influence fertility probability,
pointing to a concrete physiological marker that could be exploited in practice.

Use case evaluations indicate that the model outputs could be relevant for contraception and NFP\@.
In simulations, model-guided decisions reduced unintended pregnancies relative to naive and
calendar baselines and improved the efficiency (pregnancies per 1000 intercourse events) of timed intercourse.
These results are simulation-based and depend on assumptions about intercourse
patterns and fecundability; they should not be interpreted as clinical effectiveness estimates or as
direct comparisons to established contraceptive methods.

Despite the limitations of real-world tracking data, including missing entries, noise,
and user heterogeneity, this work underscores the potential of personalized, data-driven predictions
in digital reproductive health.
Future work should integrate additional physiological signals, expand demographic representation,
and validate models in prospective real-world settings.

In sum, this study contributes a systematic foundation for machine learning-based fertility prediction
and sets the stage for adaptive, user-tailored fertility support tools.