62 lines
2.8 KiB
TeX
62 lines
2.8 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Results}\label{sec:results}
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The previous section detailed the design and implementation of our ovulation prediction pipeline,
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including data preprocessing, feature engineering, input encoding, and the development of several deep learning architectures.
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We now present the results of our evaluation, focusing on the predictive accuracy of the proposed models across different temporal resolutions,
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cycle types, and user histories.
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Model performance is assessed using both overall metrics and biologically targeted subintervals,
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allowing for a nuanced comparison of approaches and their practical relevance to real-time fertility forecasting.
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% provide information about the training behaviour and statistic of the different models
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\subsection{Overall Model Performance Across Architectures}\label{subsec:overall_model_performance_across_architectures}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{resources/figures/results/model_performance_overview}
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\caption{Overview of the performances of all model types including the baselines on 4 selected performance metrics.}
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\label{fig:results_performance_overview_by_model_type}
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\end{figure}
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\subsection{Fertility Probability Prediction Accuracy}\label{subsec:fertility_probability_precition_accuracy}
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\subsubsection{Performance Across Fertile Window}\label{subsubsec:fert_performance_across_fertile_window}
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\subsubsection{Impact of Input Resolution}\label{subsubsec:fert_impact_of_input_resolution}
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\subsubsection{Impact of Input Window Length}\label{subsubsec:fert_impact_of_historical_context}
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\subsubsection{Comparison with Baselines}\label{subsubsec:fert_comparison_with_baselines}
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\subsection{Ovulation-Over Prediction Accuracy}\label{subsec:ov_over_prediction_accuracy}
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\subsubsection{Performance around Ovulation}\label{subsubsec:ov_over_performance_around_ovulation}
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\subsubsection{Impact of Input Resolution}\label{subsubsec:ov_over_impact_of_input_resolution}
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\subsubsection{Impact of Input Window Length}\label{subsubsec:ov_over_impact_of_historical_context}
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\subsubsection{Comparison with Baselines}\label{subsubsec:ov_over_comparison_with_baselines}
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\subsection{Stratified Analysis}\label{subsec:stratified_analysis}
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\subsubsection{Regular vs Irregular Cycles}\label{subsubsec:regular_vs_irregular_cycles}
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\subsubsection{Influence of User History Depth}\label{subsubsec:influence_of_past_user_data}
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\subsection{Use-Case Evaluation Results}\label{subsec:use_case_evaluation_results}
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\subsubsection{Contraception Use-Case Results}\label{subsubsec:use_case_contraception_results}
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\subsubsection{Pregnancy Use-Case Results}\label{subsubsec:use_case_pregnancy_results}
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% show, that past cycles might not directly be included but are indirectly included by the static features
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\subsection{Summary of Key Findings}\label{subsec:summary_of_key_findings}
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