# Applications of Machine Learning Algorithms in Geriatrics
## 6 Conclusions  
The paper is a systematic review of studies published between 1 January 2020 and 31 May 2025. These studies investigate ML-type algorithms in the field of geriatrics. The analysis reveals that the field is in an expansion phase. The annual increase in publications is approximately 18%. Interest in this field is particularly strong among researchers from the USA, China, Japan, and Germany. A comparative analysis of the four databases revealed that PubMed provides the most results regarding research related to the field of geriatrics. Scopus stands out with its academic research-based approach, offering competitive results in the Elderly category. WOS has an intermediate position with lower results than PubMed or Scopus, but including a roughly constant number across all categories. IEEE is the most restricted platform, offering databases where the number of research papers is relatively small due to its focus on the technological field. In total, the comparative analysis highlighted the very small number of results from collaborations between geriatric specialists and those in the field of ML. Among the analyzed algorithms, the most used are RF with over 52% of the total studies analyzed, followed by XGBoost with 24%, SVM with 16%, and other models such as KNN and Naive Bayes. Algorithms are used in various areas of geriatrics, such as predicting postoperative mortality, where they achieve an accuracy of up to 92.4%, detecting falls with an accuracy of up to 99%, analyzing nutritional status with a sensitivity of 91%, and classifying the level of frailty with an accuracy of 95%. The evaluation of algorithm performance is carried out using standard metrics represented by accuracy, precision, recall, sensitivity, F1-score, and AUC-ROC. Regarding accuracy, studies have reported values between 57% and 99.4%, with an average of 81.1%. Sensitivity is reported between 68% and 97%, with an average reported value of 91%. The F1-score is reported between 73% and 94%, while the AUC-ROC values range from 62.5% to 98.8%, suggesting the predictive capability of the models, especially in oncology and trauma. These results demonstrate the ability of ML algorithms in the early identification, prediction, and classification of diseases in the field of geriatrics. In addition to these results, five deficient directions in the current literature are noted, namely, the lack of standardization of the datasets, the reduced explainability of the models, the absence of longitudinal studies, the low implementation in clinical practice, and the lack of explicit treatment of ethical and privacy issues. Based on these findings, the authors recommend the creation of open databases labeled with metadata that can be studied in various research contexts, the explicit integration of algorithmic explanations into clinical decision-making processes, the conduct of prospective studies with temporal validation and long-term patient follow-up, interdisciplinary collaboration between bioethics engineers and public policy specialists, and the adaptation of legal frameworks such as the GDPR and the AI Act to support the use of AI in geriatrics. In conclusion, the paper demonstrates that ML algorithms contribute to the personalization, prediction, prevention, and adoption of tailored means of elderly care. With an average accuracy of over 80% in most clinical scenarios, these algorithms assist medical decision-making and optimize resource allocation in order to improve patients' quality of life. For the integration of these models into health systems, technical validations and responsible approaches from ethical, legal, and social perspectives are necessary, aspects that can only be addressed through increased ongoing research in this field. Conceptualization, A.S. and C.-M.R. ; methodology, A.S. and C.-M.R. ; software, C.-M.R. ; validation, A.S. and C.-M.R. ; formal analysis, A.S., C.-M.R. and E.M.I. ; investigation, A.S., C.-M.R. and E.M.I. ; resources, A.S. and C.-M.R. ; data curation, C.-M.R.