Alex Blank
28 lines
1.2 KiB
TeX
28 lines
1.2 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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Our goal was to
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Based on an extensive real-world database and established model architectures for timeseries analysis,
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we expect to outperform both rule-based baselines and related studies.
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We think, that for regular cycles, the performance difference will be lower than
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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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\section{Future Work}\label{sec:future_work}
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% inclusion of markers
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% better labeling process
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% more advanced models
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