77 lines
5.2 KiB
TeX
77 lines
5.2 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Discussion}\label{sec:discussion}
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In this study, we investigated the performance of different machine learning architectures on the task of fertility prediction,
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with the aim to find a model that performs well for natural family planning and natural contraception on regular and irregular cycles.
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Based on an extensive real-world database and established model architectures for timeseries analysis,
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we expect our models to outperform the rule-based baselines.
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We think, that for regular cycles, the performance difference will be lower than irregular cycles,
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as the baseline models have no way of adapting to irregularities.
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In general, we expect the transformer based model to outperform the LSTM basd models, as they have proven to be
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a more effective for time-series analysis tasks especially for longer sequences.
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We also expect to find similar performance on irregular cycles compared to regular cycles,
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if the temperature is a reliable predictive indicator for the ovulation.
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If the performance on irregular cycles is significantly worse, and the predicted fertility curves see no upward trend around
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the actual fertility curves, we have no reason to believe that there is any predictive value in the temperature as is.
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Results do not show any clear indication that the temperature contains any patterns useful for the prediction of
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fertility or the ovulation.
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% talk about whether bbt / temperature can be used for such a task, discuss bbt doubt papers
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% While previous work has argued against the predictive value of BBT~\cite{some_author_2010}, our findings suggest otherwise.
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%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.
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% explain the need for further medical interpretation of the results of either model
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The findings do not show a clear indication, that the temperature can be used as a predictive target.
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The results of irregular cycles should be significantly better to infer, that there are usable patters in the
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temperature readings before the ovulation or the fertile days.
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For a practical use case of any model's prediction, a medical interpretation should be performed.
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While the results themselves can give a clear indication of both the fertility probability and whether the ovulation
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of a given cycle is already over for any given day, there a variety of external factors that should be taken into
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consideration for a direct output to the user.
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\section{Future Work}\label{sec:future_work}
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There are several directions in which this study could be extended,
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most of which were omitted due to time and resource constraints but represent valuable areas for future exploration.
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One major area is feature selection.
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The dataset used includes additional user-entered markers such as physiological signs (e.g., bleeding, illness, stress)
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and external events (e.g., intercourse, pregnancy tests).
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These markers were not included in the present analysis but may carry predictive value and could meaningfully improve model performance.
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Similarly, the introduction of engineered or intermediate features, derived from raw inputs, may help models better
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capture relevant patterns and temporal dependencies.
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Additionally, the target features could be modelled in a better way, as, especially for long cycles,
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there is a large imbalance of value distribution.
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If a cycle has a length of 100 days with an ovulation at day 90, only 10\% of the ovulation-over targets are one.
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The same applies to the fertility target, which will be zero throughout almost the whole sequence,
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which will make it harder for the models to learn useful information.
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Alternative target formulations could also be explored to better reflect the structure of the fertile window and ovulation.
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For example, instead of predicting a daily fertility probability, models could aim to identify the absolute day of ovulation,
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or estimate the time until the next (or since the last) ovulation event.
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As noted in Section~\ref{subsubsec:data_labeling}, ovulation labels were assigned using a
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retrospective algorithm trained on expert-annotated data.
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Any inaccuracies in this algorithm propagate directly to the supervised learning labels.
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Thus, improving prediction quality may require a newly labeled dataset—ideally combining expert review with algorithmic assistance.
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Finally, while the models used in this study (LSTMs and Transformers) are well-established for time-series analysis,
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they were not extensively customized.
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Future work could involve tailoring architectures more specifically to the characteristics of menstrual cycle data.
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Recent transformer variants designed for time series, such as \emph{TimeXer}~\cite{wang_timexer_2024} or \emph{MAMBA}~\cite{wang_is_2024},
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may offer improved performance.
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Alternatively, a custom architecture could be developed to better reflect the domain-specific structure of biological temperature data.
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Future work may incorporate more advanced hyperparameter optimization techniques,
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such as Bayesian Optimization, Genetic Algorithms, or Neural Architecture Search (NAS),
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to better explore the joint parameter space in a more efficient and principled manner. |