# Applications of Machine Learning Algorithms in Geriatrics
## 5 Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Objective** & **Dataset** & **ML Model** & **Metrics** \\ \hline
[75] & Predict cardiovascular aging for \(\geq\)65 year old. & 800 patients, Almaty; clinical and behavioral data & k-means, RF & Accuracy = 98.8\%; AUC-ROC = 0.989 \\ \hline
[76] & Assess locomotive syndrome via FISTS and IMU & 144 features from IMU (pelvis), GLFS-25 labels & MLP, PCA (best) & Accuracy = 90\%; F1-score = 91\% \\ \hline
[63] & Identify sarcopenia markers in elderly & 1971 adults, +65 year old, DXA-based diagnosis & RF, LR & Accuracy = 95\%, Sensitivity 1, Specificity = 0.94 \\ \hline
[77] & Predict PIMs in geriatric outpatients & 11,741 prescriptions, 41 PIM types, China & CC, CatBoost (best), XGBoost, TabNet etc. & Accuracy = 97.83\%, F1-score = 90.69\% \\ \hline
[78] & Compare ML models for TBI prognosis in elderly & MIMIC-III, adults +65 year old, TBI, training set and testing set ratio = 7:3 & AdaBoost, RF, LR & AUC: 0.799 (AdaBoost); AUC: 0.795 (RF); AUC: 0.792 (LR) \\ \hline
[79] & Predict sarcopenia reversal after exercise & 90 older adults, CT scans & Stacking (best of 11 models) & Accuracy = 85.7 \(\pm\) 10.6\%, F1-score = 75.3 \(\pm\) 11.5\% \\ \hline
[80] & Detect geriatric depression and anxiety & 352 patients, 60–90 years old, EWUMH & RFC, GBC, SVC, LRC, Multi-Label Classification & Loss = 0.0000 for RFC (depression)—RFC (anxiety), GBC (depression)—RFC (anxiety) \\ \hline
[81] & Detect nocturnal wandering for individuals in elderly & 26 elderly, 2762 bed-exit events & eNightLog & Accuracy = 99.8\%, Precision = 99.6\%, Sensitivity = 100\%, Specificity = 99.6\% \\ \hline
[82] & Early detection of locomotive decline & 13 kinematic features (squat features and one-leg standing features) & ANN, RF Regressor & R\({}^{2}\) up to 0.86 (RF Regressor) \\ \hline
[54] & Predict delirium episodes in hospitalized elderly & 78 patients, 1149 observations & RF, LR & R\({}^{2}\) = 75.8\%, RMSE = 3.29 \\ \hline
[31] & Predict frailty from Kinect skeleton data & 787 elderly, functional exercises (chair stands etc.) & KNN, SVC, MLP, BC, VC & Accuracy = 97.5\% (SVC, MLP) \\ \hline
[83] & Identify cardiac chest pain predictors & 258 geriatric patients (60-year-old average), MNHT Mexico, 27 variables & LR & Accuracy = 96.4\%; F1-score = 91.2\% \\ \hline
[84] & Human joint detection in depth images dataset & Custom depth image (adapted HRNet) & HRDepthNet & Precision = 92\%; Recall = 88.2\%; F1-score = 90.1\% \\ \hline \end{tabular}
\end{table} 
Table 10: ML research in geriatric healthcare disseminated in MDPI. The results presented in Table 10 show that ML algorithms are tools that enable continuous monitoring, early detection, the prevention of certain behaviors, and the personalization of investigations in geriatrics. However, the lack of detailed metrics in some studies indicates the need for standardization in evaluating these models. Moreover, the lack of dataset standardization, and, on the other hand, the need to increase the volume of research, as can be seen in Table 10, which shows a very small number of results, suggest the need to encourage researchers to explore this area. Overall, this research outlines a direction for integrating ML methods into prevention, assistance, and detection systems, as well as personalization in healthy aging. The factors influencing the field of geriatrics are analyzed in various processes targeting environmental factors, also addressed through ML methods, such as water quality [86; 87] or air quality [88]. On the other hand, digital systems that introduce data scaling methods at the sensory level [89], implemented with embedded microcontrollers [90], allow for the remote monitoring of the elderly. Thus, two research directions are defined. The first direction focuses on research related to environmental factors that influence the quality of life of the elderly, while the second direction directly impacts how the elderly are monitored. Thus, from the analysis of the papers, the following are observed:  
1.