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Alex Blank
2025-09-09 13:44:45 +00:00
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thesis/sections/related_work.tex
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\section{Related Work}\label{sec:related_work}
This section will introduce related work of both ovulation detection and ovulation prediction.
First, we'll introduce early work on the detection of the ovulation based on biomarkers.
Then, we'll show how others have used body temperature to predict ovulation and what their limitations are.
Lastly, we will take a closer look at related work that uses other biomarkers as base, or as an addition to the body
First, I will introduce early work on the detection of the ovulation based on biomarkers.
Then, I will show how others have used body temperature to predict ovulation and what their limitations are.
Lastly, I will take a closer look at related work that uses other biomarkers as base, or as an addition to the body
temperature for ovulation and fertility prediction.
A variety of approaches have historically been explored for ovulation detection and prediction,
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Results show an edge for the random forest model with a reported 87\% accuracy and AUC-ROC (area under the receiver operating characteristic curve)
of 0.96 for the three class approach.
The four class approach significantly reduced accuracy to 68\% and AUC-ROC of 0.77.
There was no separation into cycle groups and all cycles were in a regular group, with a mean lengths of 28 days (SD: 1.65).
There was no separation into cycle groups and all cycles were in a regular group, with a mean length of 28 days (SD: 1.65).
There are some additional studies based on commercial products, such as \emph{Oura Ring}\cite{thigpen_oura_2025}
or \emph{Natural Cycles}\cite{bull_real-world_2019} that work with temperature data as a base.