alex/temperature-based-fertility-prediction-thesis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fixes

Browse Source
This commit is contained in:
Alex Blank
2025-08-12 19:00:08 +00:00
parent 5655022d74
commit f9b1b63a12
3 changed files with 158 additions and 133 deletions
Download Patch File Download Diff File Expand all files Collapse all files
thesis/sections/discussion.tex
+28 -25
View File
@@ -4,24 +4,24 @@
\section{Discussion}\label{sec:discussion}
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.
Based on an extensive real-world database and established model architectures for timeseries analysis,
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.
%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.
%
%
%Based on an extensive real-world database and established model architectures for timeseries analysis,
%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
@@ -30,14 +30,17 @@ fertility or the ovulation.
% explain the need for further medical interpretation of the results of either model
The findings do not show a clear indication, that the temperature can be used as a predictive target.
The results of irregular cycles should be significantly better to infer, that there are usable patters in the
temperature readings before the ovulation or the fertile days.
% transformer can deal with more data and does also perform better for some cases
% lstm outperforms
For a practical use case of any model's prediction, a medical interpretation should be performed.
While the results themselves can give a clear indication of both the fertility probability and whether the ovulation
of a given cycle is already over for any given day, there a variety of external factors that should be taken into
consideration for a direct output to the user.
%The findings do not show a clear indication, that the temperature can be used as a predictive target.
%The results of irregular cycles should be significantly better to infer, that there are usable patters in the
%temperature readings before the ovulation or the fertile days.
%
%For a practical use case of any model's prediction, a medical interpretation should be performed.
%While the results themselves can give a clear indication of both the fertility probability and whether the ovulation
%of a given cycle is already over for any given day, there a variety of external factors that should be taken into
%consideration for a direct output to the user.
\section{Future Work}\label{sec:future_work}