alex/temperature-based-fertility-prediction-thesis
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Alex Blank
2025-09-09 13:44:45 +00:00
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@@ -54,8 +54,9 @@ This allows for more reliable detection of subtle thermal shifts associated with
This study aims to advance fertility prediction by leveraging an extensive database of more than 40,000 menstrual cycles,
covering cycle lengths 11 to 149 days and ovulation days ranging from 7 to 136, recorded using
an intravaginal wearable device that continuously measures core body temperature at a resolution of 288 measurements per day.
The objective is to develop a machine learning model that performs reliably across diverse cycle types, including irregular ones.
To this end, we compare a set of time series-based machine learning architectures and evaluate their performance for
The objective is to develop a machine learning model that predicts the fertility for any given day and
performs reliably across diverse cycle types, including irregular ones.
To this end, I will compare a set of time series-based machine learning architectures and evaluate their performance for
NFP and contraception.
This thesis addresses this gap by providing the first large-scale, systematic comparison of LSTM and