alex/temperature-based-fertility-prediction-thesis
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Alex
2025-08-06 15:14:19 +02:00
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thesis/sections/discussion.tex
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In this study, we investigated the performance of different machine learning architectures on the task of fertility prediction,
with the aim to find a model that performs well for natural family planning and natural contraception on regular and irregular cycles.
Our goal was to
Based on an extensive real-world database and established model architectures for timeseries analysis,
we expect to outperform both rule-based baselines and related studies.
We think, that for regular cycles, the performance difference will be lower than
we expect our models to outperform the rule-based baselines.
We think, that for regular cycles, the performance difference will be lower than irregular cycles,
as the baseline models have no way of adapting to irregularities.
In general, we expect the transformer based model to outperform the LSTM basd models, as they have proven to be
a more effective for time-series analysis tasks especially for longer sequences.
We also expect to find similar performance on irregular cycles compared to regular cycles,
if the temperature is a reliable predictive indicator for the ovulation.
If the performance on irregular cycles is significantly worse, and the predicted fertility curves see no upward trend around
the actual fertility curves, we have no reason to believe that there is any predictive value in the temperature as is.
Results do not show any clear indication that the temperature contains any patterns useful for the prediction of
fertility or the ovulation.
% talk about whether bbt / temperature can be used for such a task, discuss bbt doubt papers
@@ -30,7 +40,6 @@ of a given cycle is already over for any given day, there a variety of external
consideration for a direct output to the user.
\section{Future Work}\label{sec:future_work}
There are several directions in which this study could be extended,
most of which were omitted due to time and resource constraints but represent valuable areas for future exploration.
@@ -39,8 +48,13 @@ One major area is feature selection.
The dataset used includes additional user-entered markers such as physiological signs (e.g., bleeding, illness, stress)
and external events (e.g., intercourse, pregnancy tests).
These markers were not included in the present analysis but may carry predictive value and could meaningfully improve model performance.
Similarly, the introduction of engineered or intermediate features—derived from raw inputs—may help models better
Similarly, the introduction of engineered or intermediate features, derived from raw inputs, may help models better
capture relevant patterns and temporal dependencies.
Additionally, the target features could be modelled in a better way, as, especially for long cycles,
there is a large imbalance of value distribution.
If a cycle has a length of 100 days with an ovulation at day 90, only 10\% of the ovulation-over targets are one.
The same applies to the fertility target, which will be zero throughout almost the whole sequence,
which will make it harder for the models to learn useful information.
Alternative target formulations could also be explored to better reflect the structure of the fertile window and ovulation.
For example, instead of predicting a daily fertility probability, models could aim to identify the absolute day of ovulation,