# Applications of Machine Learning Algorithms in Geriatrics
## 5 Discussions
The most frequently studied ML models are RF, XGBoost, and SVM. These are predominantly analyzed in the geriatric context due to their ability to operate with incomplete data. This behavior provides accurate results in order to manage complex clinical scenarios. 2. The subdomains of geriatrics applicable in ML include multiple applicable areas such as predicting postoperative mortality, classifying the level of frailty, identifying fall risk, monitoring nutritional status, analyzing pain, the early detection of dementia, managing patients with renal or cardiovascular insufficiency, and analyzing depression among the elderly. 3. The performance metrics of ML algorithms, predominantly analyzed in contribution articles, are accuracy, precision, F1-score, and AUC-ROC. In the analyzed literature, the reported values exceed 80% in most applications, which confirms the utility of the models in clinical practice. Additionally, these values also suggest the possibility of improving the metrics if specialists continue to investigate other supplementary algorithms, in addition to those preferred so far. 4. The geographical distribution of research indicates a concentration of scientific activity in developed countries such as the USA, China, Japan, and Germany, but also a gradual openness towards interdisciplinary international collaborations. 5. The type of publications shows an increased interest in empirical articles, as opposed to purely theoretical ones, an aspect highlighted by the maturation of the field and the focus on concrete clinical applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Objective** & **Dataset** & **ML Model** & **Metrics** \\ \hline \multirow{3}{*}{[85]} & Real-time human & Body-mounted sensor, & & \\  & motion tracking in & 6 h of data, 3 subjects, & NN & Accuracy \(\approx\) 12 ms \\  & natural settings & \(\sim\)40,000 steps & & \\ \hline \hline \end{tabular} Note: AdaBoost—Adaptive Boosting; BC—Bagging Classifier; CC—Classifier chains; CT—Computed tomography; DXA—Dual-energy X-ray absorptiometry; EWUMH—Ewha Womans University Mokdong Hospital; FTSTS—Five-Time Sit-to-Stand Test; GBC—Gradient-boosting classifier; HRDepthNet—High-Resolution Depth Net; HRNet—High-Resolution Net; IMU—Inertial Measurement Unit; LRC—Logistic regression classifier; MIMIC—Medical Information Mart for Intensive Care; MNHT—Medical Norte Hospital in Tijuana; PCA—Principal component analysis; PIM—Potentially inappropriate medications; RFC—Random forest classifier; RMSE—Root Mean Square Error; TBI—Traumatic brain injury; VC—Voting Classifier.  
\end{table} 
Table 10: _Cont._In relation to the RQ, the study responds as follows:  
* RQ1: ML algorithms are applied in various subfields of geriatrics. In the specialized literature, the postoperative mortality prediction, fall risk identification, hospitalization duration estimation, frailty level classification, nutritional status assessment, early dementia detection, and analysis of depression and overall functional status in the elderly are highlighted. These directions reflect the need for personalized care in a complex clinical context. * RQ2: The purposes of using ML algorithms are divided between classification, with directions such as differential diagnosis, functional status labeling, and prediction in applications for disease progression, recurrence risk, or post-intervention survival. Out of the total number of analyzed works, 40% aimed at classification, while 60% focused on prediction, indicating a trend towards anticipatory clinical applicability. * RQ3: The most commonly used metrics for performance evaluation are accuracy, precision, sensitivity, the F1-score, and AUC-ROC. These are directly correlated with the potential for the practical integration of applications in real-world contexts. The reported performances show that the models can support clinical decision-making, provided they are used for assistance and not for automatic decision-making in existing healthcare systems.