# Applications of Machine Learning Algorithms in Geriatrics
## 4 Bibliometric Analysis
### Overview of ML Studies in Geriatrics  
Table 9 presents a summary of scientific studies that use ML algorithms in the field of geriatrics. For each study, information was extracted regarding the dataset size and type, the validation method applied, the performance metrics obtained during the training and testing phases, and comments made on the possibility of overfitting. The synthesis provides an overview of how learning models are developed and validated in the context of an aging population.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & \
\begin{tabular}{c} **Dataset** \\ **Size** \\ \end{tabular} & **Dataset Type** &
\begin{tabular}{c} **Validation** \\ **Method** \\ \end{tabular} & **Training Metrics** & **Testing Metrics** \\ \hline
[60] & 50,366 & \
\begin{tabular}{c} ICU; illness severity \\ (USA, The Netherlands) \\ \end{tabular} &
\begin{tabular}{c} Internal, \\ external, \\ temporal \\ \end{tabular} & AUC-ROC = 0.866 & AUC-ROC = 0.838–0.884, \\ \hline
[61] & 1074 &
\begin{tabular}{c} NHANES; sleep \\ efficiency variability \\ and cognitive \\ function (USA) \\ \end{tabular} & Regression analysis & Pearson r = \(-\)0.63 & \(\beta\) = \(-\)2.01 (DSST) \\ \hline
[62] & 740 & \
\begin{tabular}{c} Hospital cohort; \\ delirium risk in \\ geriatric (China) \\ \end{tabular} &
\begin{tabular}{c} 5-fold cross- \\ validation \\ \end{tabular} & AUC = 0.967 & AUC = 0.950, \\ \hline
[63] & 1971 &
\begin{tabular}{c} NIGSBRH; sarcopenia \\ (Italy) \\ \end{tabular} & RF, LR & - & Accuracy = 95\%, \\ \hline
[64] & 833 & \
\begin{tabular}{c} NHANES; \\ audiometric and \\ blood data (USA) \\ \end{tabular} &
\begin{tabular}{c} Algorithm \\ comparison (RF best) \\ \end{tabular} & - & AUC = 0.834 \\ \hline
[65] & 63 &
\begin{tabular}{c} Cohort group; gut \\ microbiome and \\ metabolome (China) \\ \end{tabular} & ML classifier & - & AUC = 0.7083–0.8833 (gut bacteria), \\ \hline
[66] & 2660 & \
\begin{tabular}{c} GLIM and \\ GNSSPEN; \\ hospitalized elderly \\ with malnutrition \\ (China) \\ \end{tabular} &
\begin{tabular}{c} Train/test \\ split, 5 ML \\ models \\ \end{tabular} & AUC = 0.791–925 & AUC = 0.863–0.950 \\ \hline
[67] & 450 &
\begin{tabular}{c} INSCOC; \\ malnutrition in \\ elderly cancer \\ patients (China) \\ \end{tabular} & 9 ML models tested & - & AUC = 0.945 (XGBoost) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Datasets, validation methods, and performance metrics comparison.  
The authors note that this comparison is difficult because the datasets are heterogeneous. Essentially, these include different data sources, with major variations in both volume and content. The clinical goals are also distinct because some studies aim to predict mortality, while others focus on malnutrition, sarcopenia, frailty, or depression. The fact that these studies target different predictions implies differences in the input variables and target labels. The lack of metric standardization also makes a direct comparison between studies impossible [74]. Not all studies report the same metrics, and some completely omit the intermediate results. Under these conditions, Table 9 provides a comparative overview, but interpreting the differences between the studies is not straightforward because the methodological and clinical context of each article varies.