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    \begin{titlepage}
        \centering
        {\large \textbf{Leipzig University}}\\[1.5cm]
        {\large Faculty of Mathematics and Computer Science}\\[1cm]

        \includegraphics[width=7cm]{leipzig_university_logo}\\[1cm] % Adjust size as needed

        {\huge \textbf{Body-Core Temperature based Fertility Prediction with Machine Learning}}\\[1.5cm]

        \textbf{Master’s Thesis}\\[1cm]

        \textbf{Author:}\\
        Alexander Blank\\ [0.2cm]
        Matriculation Number: \\
        3796505\\ [0.5cm]

        \textbf{Supervisor:}\\
        Prof. Bogdan Franczyk\\
        Dr. Christian Alvermann\\[1.5cm]

        \textbf{Date:} September 2025\\[1.5cm]

        \textbf{Master of Science: Data Science}\\
        \textbf{Leipzig University}\\[1cm]
        \textit{In collaboration with VivoSensMedical GmbH}
    \end{titlepage}

    \pagebreak
    \begin{abstract}
        This thesis investigates the feasibility of predicting fertility from high-resolution body
        core temperature data using machine learning.
        More than 40,000 cycles recorded with an intravaginal sensor were used to train and evaluate recurrent,
        transformer-based, and convolutional model variants.
        Results show that while LSTM models achieved the lowest error on standard metrics,
        transformer-based architectures performed more robustly in use case evaluations
        for contraception and natural family planning (NFP).
        Predictions were substantially more accurate in regular cycles than in irregular ones,
        underlining both the promise and the limits of temperature-based approaches.
        A pre-ovulatory temperature drop was identified as a relevant physiological marker influencing fertility probability.
        In simulated use cases, model predictions substantially reduced simulated pregnancy risk compared to
        baseline methods and improved timing for conception efforts.
        Despite the limitations of retrospective labeling, noisy real-world data,
        and the restriction to temperature as a single modality,
        the study demonstrates that machine learning can meaningfully advance individualized fertility prediction.
        These results provide a foundation for future multimodal approaches and for the development of adaptive, user-tailored fertility support tools.
    \end{abstract}
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    \include{sections/related_work}

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    \include{sections/results}

    \include{sections/discussion}

    \include{sections/conclusion}

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    \include{sections/appendix}


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    \section*{Declaration of Use of AI-Assisted Writing Tools}

    Parts of this thesis were prepared with the assistance of generative AI tools, including OpenAI’s ChatGPT .
    These tools were used to improve clarity, coherence, and fluency of the written language,
    to rephrase technical explanations for greater precision, and to support structural editing.

    At no point were generative models used to produce scientific content, generate results, or conduct analysis.
    All AI-generated suggestions were critically reviewed, adapted, and verified by me to ensure their accuracy and relevance.

    The outputs served solely as editorial support and did not replace my own academic work or authorship.
    Responsibility for the content, structure, and conclusions of this thesis remains entirely my own.

    \pagebreak
    Ich versichere, dass ich die vorliegende Arbeit selbstständig und nur unter Verwendung der
    angegebenen Quellen und Hilfsmittel angefertigt habe, insbesondere sind wörtliche oder
    sinngemäße Zitate als solche gekennzeichnet. Mir ist bekannt, dass Zuwiderhandlung auch
    nachträglich zur Aberkennung des Abschlusses führen kann.
    Ich versichere, dass das elektronische Exemplar mit den gedruckten Exemplaren übereinstimmt.

    \vspace{2cm}

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    \begin{tabular*}{\textwidth}{@{\extracolsep{\fill}} l c r}
        Ort: & Datum: & Unterschrift: \\
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