Alex Blank
35 lines
2.1 KiB
TeX
35 lines
2.1 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Conclusion}\label{sec:conclusion}
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This thesis presented a systematic investigation of different machine learning architectures
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for fertility prediction based on high-resolution body core temperature data.
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By comparing LSTM- and Transformer-based models, as well as their convolutional variants,
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the results show that machine learning can achieve high predictive performance.
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LSTM models performed best according to standard evaluation metrics, whereas Transformer-based models
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proved more robust in simulated use-case evaluations for contraception and Natural Family Planning (NFP).
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Predictions were consistently more reliable in regular cycles than in irregular ones,
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highlighting both the potential and the inherent limits of temperature-based approaches.
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A characteristic pre-ovulatory temperature drop was identified as correlating with fertility.
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Both its timing and its magnitude appear to influence fertility probability,
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pointing to a concrete physiological marker that could be exploited in practice.
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Use-case evaluations indicate that the model outputs could be highly relevant for contraception and NFP\@.
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In simulations, pregnancy rates approached those reported for commonly used contraceptives such as condoms
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or oral contraceptives, and with simple additional measures could even approximate the effectiveness of
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long-term hormonal methods or sterilization.
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While pregnancy rates for the NFP use-case were not significantly improved,
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the predictions enabled a four-fold increase in the efficiency of timed intercourse,
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facilitating more targeted pregnancy efforts for couples.
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Despite the limitations of real-world tracking data, including missing entries, noise,
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and user heterogeneity, this work underscores the potential of personalized, data-driven predictions
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in digital reproductive health.
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Future work should integrate additional physiological signals, expand demographic representation,
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and validate models in prospective real-world settings.
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In sum, this study contributes a systematic foundation for machine learning-based fertility prediction
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and sets the stage for adaptive, user-tailored fertility support tools. |