further work on methodology

thesis/sections/related_work.tex

@@ -3,13 +3,30 @@
 
 
 \section{Related Work}\label{sec:related_work}
-There has been a variety of works in menstrual cycle analysis and ovulation prediction based on different physiological signs.
+
+A variety of approaches have historically been explored for ovulation detection and prediction,
+ranging from hormonal assays to physiological signal tracking.
+
+In~\citeyear{wallach_prediction_1980}, \citeauthor{wallach_prediction_1980} identified several physiological indicators for ovulation timing,
+including salivary ferning and viscosity, serum levels of progesterone and estrogen,
+and urinary luteinizing hormone (LH) concentrations~\cite{wallach_prediction_1980}.
+These indicators showed strong correlation with ovulation timing as measured via transvaginal ultrasound.
+\citeauthor{noauthor_monitoring_1987} later expanded on this work by focusing specifically on LH and estradiol,
+confirming that LH surges reliably indicate an imminent ovulation event.
+
+Despite their diagnostic value, many of these biomarkers are difficult to measure continuously and reliably in everyday settings,
+limiting their practicality for real-time or large-scale applications.
 
 \subsection{Temperature-Based Approaches}\label{subsec:temperature_based_approaches}
+Body temperature has emerged as a more accessible physiological signal for ovulation tracking,
+given the feasibility of continuous and non-invasive measurement.
+As outlined in Section~\ref{subsubsec:physiological_signs}, basal body temperature (BBT) exhibits a biphasic pattern across
+the menstrual cycle that correlates with ovulation.
 
-Several studies have questioned the utility of BBT (Basal Body Temperature) for reliable ovulation prediction.
-For example \citeauthor{bauman_basal_1981} concluded, that BBT is not a robust standalone marker due to its
-retrospective nature and sensitivity to external factors and thus must be used with extreme caution clinical or research evaluations~\cite{bauman_basal_1981}.
+However, several studies have raised concerns about its reliability as a predictive marker.
+\citeauthor{bauman_basal_1981} concluded that BBT alone is insufficiently robust due to its retrospective nature and
+high susceptibility to external confounders, recommending caution in its clinical or research use~\cite{bauman_basal_1981}.
+Similarly, \citeauthor{moghissi_accuracy_1976} emphasized its limited accuracy, particularly in cycles with irregularities~\cite{moghissi_accuracy_1976}.
 
 However, such conclusions were largely based on the standard BBT method, which relies on a single-point measurement taken
 immediately upon waking—typically reflecting the body's lowest resting temperature.
@@ -72,7 +89,6 @@ These findings suggest that electrical resistance is a strong physiological mark
 A related modern implementation is the commercial product \textit{kegg}~\cite{noauthor_kegg_nodate}, which measures the electrical resistance of cervical mucus.
 The device uses an undisclosed algorithm to estimate fertility status based on these readings, although no peer-reviewed validation studies are currently available.
 
-
 \citeauthor{masuda_machine_2025} developed a machine learning algorithm to classify phases of the menstrual cycle
 (follicular vs. luteal) based on sleeping heart rate, as recorded by a fitness tracker~\cite{masuda_machine_2025}.
 They used an XGBoost classifier for this binary task and additionally performed ovulation day prediction,
@@ -100,8 +116,7 @@ waking—they report classification accuracies between 0.843 and 0.864, dependin
 with very similar numbers for precision, recall, specificity and F1 score.
 Ovulation day prediction yielded an average absolute error between 3.6 and 4.1 days.
 
-When models are trained on highly constrained datasets with predictable patterns and clear ovulatory signals,
-complex methods often show limited gains over naive or rule-based approaches—as will be demonstrated in Section~\ref{sec:methodology}.
+
 
 \paragraph{Summary:}
 While various physiological signals and modeling strategies have been explored for ovulation prediction,