# Applications of Machine Learning Algorithms in Geriatrics
## 5 Discussions
From the analyzed articles, 10 papers were selected that the authors consider representative of the field of geriatrics. These were qualitatively evaluated based on the dataset details, performance metrics, and clinical applicability. The scores ranged from 0 to 2, with 0 representing vague, 1 partially detailed, and 2 well-detailed (Table 11). The qualitative analysis is supported by 10 papers that evaluate the three reference indicators for ML tools based on the authors' expertise. Table 11 summarizes this evaluation based on the total score obtained for each criterion individually. This approach helps in interpreting the results regarding the need for the current systematic review. The differences between the performance indicator values for AUC range from 0.57 to 0.98. These variations are justified by the type and quality of datasets used, the class imbalance, the degree of preprocessing applied before model training, or the complexity of the tasks addressed. Models that handle simple tasks, such as binary classification with clinical variables, perform better. Tasks involving heterogeneous variables, incomplete data, insufficient data volume, or highly complex clinical scenarios, such as predicting rare or multidimensional events, often result in lower performance. Therefore, variations in AUC reflect the quality of the model and the specific challenges inherent to each clinical application. The paper is addressed to researchers in the field of ML and AI, who can identify, in this work, the future directions they should focus on in the study of algorithms in the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dataset** & **Performance Metrics** & **Clinical Applicability** & **Total** \\ \hline
[15] & 2 & 0 & 2 & 4 \\
[26] & 2 & 2 & 2 & 6 \\
[28] & 2 & 2 & 2 & 6 \\
[30] & 2 & 2 & 2 & 6 \\
[31] & 2 & 2 & 1 & 5 \\
[39] & 2 & 2 & 2 & 6 \\
[41] & 1 & 1 & 1 & 3 \\
[45] & 1 & 2 & 2 & 5 \\
[49] & 1 & 2 & 2 & 5 \\
[52] & 2 & 2 & 1 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Evaluating the quality of studies. geriatric field. They can also understand the technical challenges specific to this data. The paper is addressed to healthcare professionals and medical staff, who can study how these technologies can be integrated into their practice to improve clinical decisions. University faculty and PhD students can use this study as a systematic database for developing future research papers or interdisciplinary projects. In the private sector and the medical industry, ML solutions with the potential for scaling and implementation in portable devices, mobile applications, modern devices, or integrated digital health systems can be identified. The authors believe that ML models are used in the field of geriatrics to improve diagnosis, prognosis, prevention, and the personalization of care for aging patients. Transparency regarding how these models provide suggestions is a major challenge at the moment. In the context of clinical decision-making, integrating tools like Explainable AI (XAI) into the prediction process represents a necessary future research direction to enable the understanding of the factors influencing model predictions. Implementing such mechanisms will increase clinicians' confidence in using ML technologies. This process will facilitate its widespread adoption in medical practice. The gaps identified in the literature represent starting points for future research. These address the following aspects:  
1. The lack of standardization of datasets makes it impossible to replicate studies and objectively compare the performance between the models studied. 2. The explainability of models refers to the fact that, in most cases, the papers predominantly address quantitative metrics without providing an insight into the interpretability of the models for medical staff. In the context of geriatrics, where decisions often involve high risks, explainability should be a benchmark for future research. 3. Longitudinal studies are validated on cross-sectional data sets.