# Applications of Machine Learning Algorithms in Geriatrics
## 3 ML Models Used in the Diagnosis and Monitoring of
### Metrics for Evaluating the Performance of ML Models  
The comparative evaluation of algorithms is carried out through evaluation metrics. Depending on the type of algorithm, these metrics are distinctive. The most studied metrics are accuracy, sensitivity (recall), specificity (precision), F1-score, and AUC-ROC. Accuracy is the simplest metric for evaluating a classification model. This expresses the total number of correct predictions. Regarding recall, it indicates the model's ability to correctly identify positive cases. In the medical field, false negative rates have serious consequences, which is why the authors of this paper believe that sensitivity should be a mandatory indicator regarding the quality of the developed model. Specificity reflects the model's ability to correctly identify negative cases. The value of this indicator suggests the need for adjustments in the classification model. The F1-score indicator is the harmonic mean between precision and recall. AUC-ROC is one of the most important metrics in evaluating classification models, for which an AUC value close to 1 indicates a model considered to be perfect. Based on the information presented in the geriatric literature, Table 5 shows the AUC graph. This is one of the most important metrics for the comparative evaluation of a classification model's performance. This is due to the indicator's ability to show whether the model correctly separates positive classes from negative ones. In the field of geriatrics, AUC evaluates the effectiveness of predicting adverse events, such as mortality or postoperative survival. A considerable variation in AUC values can be observed from the presented data in Table 5. This variation reflects the diversity of problems addressed, ranging from binary classification to complex predictions. The best results have a value close to 0.98, indicating that the model is performing well. Depending on the complexity of the presented application, most results vary between 0.8 and 0.9, which can indicate very good results, depending on the context. Regarding accuracy, Table 6 presents the results obtained from the contributions synthesized from the literature. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **References** \\ \hline XGBoost & [58] \\ RF & [30,54] \\ LR & [30] \\ LASSO Regression & [24] \\ Generalized Additive Model & [24] \\ LightGBM & [40] \\ CatBoost & [40] \\ XGBoost & [26] \\ DT & [26] \\ SVM & [14] \\ \hline \hline \end{tabular} Note: LASSO—Least Absolute Shrinkage Selection Operator.  
\end{table} 
Table 4: Prediction models investigated in the literature for geriatric purposes. Accuracy shows the proportion of predictions made correctly by the model. This metric can be considered for evaluating the quality of the model only when the dataset is balanced. From these considerations, the quality of the dataset directly influences the quality of the model. In geriatrics, accuracy should be treated as a primary metric when there are no disparities between positive and negative classes. In Table 6, the data show a wide range of values, from 57% to 99.4%, reflecting significant differences between the types of issues addressed. The best results were obtained around the value of 99.4%, indicating extremely precise models. Some studies have reported more modest values, around 69.4% or 70%. These suggest possible issues related to data variability, task complexity, dataset quality, or model integration difficulty. Most of the values are concentrated between 80 and 90%, with the most frequent being 81.1%. This is considered a decent value for problems with a high degree of classification difficulty. Recall measures the model's ability to correctly detect positive cases. This indicator should be a benchmark in the medical context, where a negative diagnosis, that is, a false or incorrect one, can have serious consequences. For example, incorrectly diagnosing chronic kidney failure can have devastating consequences for the patient.