hopefully final commit

thesis/sections/conclusion.tex

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-\section{Conclusion}\label{sec:conclusion}
+\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 highly relevant for contraception and NFP\@.
+In simulations, pregnancy rates approached those reported for commonly used contraceptives such as condoms
+or oral contraceptives, and with simple additional measures could even approximate the effectiveness of
+long-term hormonal methods or sterilization.
+While pregnancy rates for the NFP use-case were not significantly improved,
+the predictions enabled a four-fold increase in the efficiency of timed intercourse,
+facilitating more targeted pregnancy efforts for couples.
+
+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.