# Applications of Machine Learning Algorithms in Geriatrics
## 3 ML Models Used in the Diagnosis and Monitoring of
### The Most Studied ML Models in Recent Literature
The impact of intergenerational relationships, demographic factors, lifestyle, and health on depression among Chinese elderly, with an emphasis on the link between interactions with children and depressive symptoms, is investigated by [PERSON] et al. [39]. The correlations are studied using the RF algorithm, which achieved an accuracy of 98%, indicating the ability to predict such events in order to combat them. Moreover, in the field of estimating the number of hospital days, this time for patients with fractures, it is examined through a multitude of ML algorithms. The Wide and DL model proposed by [PERSON] et al. [40] achieved the best performance with an AUC-ROC of 84%, an accuracy of 79%, a precision of 73%, and a recall of 62%. This model is followed by the Light Gradient-Boosting Machine (LightGBM) and Artificial Neural Network (ANN). [PERSON] et al. [41] developed an ML model for identifying the hospital stay duration. The SVM model achieved the best performance, with an accuracy of 94.53%, an F1-score of 82.10%, a sensitivity of 72.55%, and a specificity of 76.47%. [PERSON] et al. [42] conducted a retrospective study to predict the length of hospital stay after single-segment lumbar spinal fusion. Several algorithms were investigated in this research, with the best performance achieved by Categorical Boosting (CatBoost). Another issue for patients who belong to the geriatric population concerns gastroenterology. The paper by [PERSON] et al. [41] assists medical staff in evaluating the tolerability of the probiotic Bacillus coagulans in alleviating indigestion. This issue can also be modeled using various ML algorithms [43]. [PERSON] et al. [44] focus on oncology for the classification of geriatric breast cancer. The algorithm with the best results was OneClass Detection, for which the AUC value was 71%. The prediction of survival in elderly patients with squamous esophageal cancer based on oxidative stress biomarkers is investigated by [PERSON] et al. [45], where, again, RF achieved the best results with an accuracy of 88.5%, a recall of 94.3%, and an F1-score of 91.3%. From the perspective of cancers, they are also investigated using ML algorithms, taking into account various combinations of patient characteristics [46]. In hematology, classification algorithms can identify events associated with platelets or hemorrhagic events, as presented by [PERSON] et al. [47]. The anemia of hospitalized elderly patients is investigated by [PERSON] et al. [48], achieving a detection rate of 66.7%. Regarding the evaluation of the impact of prediction models for anticoagulants in geriatric patients with cranio-cerebral trauma, [PERSON] et al. [49] investigated this using the RF algorithm, which achieved an accuracy of 95% in predicting the type of anticoagulant. Another direction that can also be explored using ML models focuses on gerontology, with an emphasis on the quality of life for the elderly. For this purpose, [PERSON] et al. [50] identified the RF algorithm as having an exceptional classification capability, achieving an accuracy of 94%, an F1-score of 94%, a sensitivity of 95%, and a specificity of 94%. In the paper by [PERSON] et al. [51], geriatric patients are investigated in the detection of COVID-19 using predictive markers of mortality. The Receiver Operating Characteristic (ROC) analysis in this case achieved a sensitivity of 57.1% and a specificity of 70.5%. [PERSON] al. [52] investigate the early detection of dementia using lifestyle-associated parameters for non-invasive screening. The RF model achieved the best results with an accuracy of 76%, an F1-score of 86%, and a recall of 100%. Most studies in the field of geriatrics investigate multiple ML models, but the best results are reported by the RF algorithm [53; 54; 55; 56], a fact highlighted in Table 1, where a comparative analysis of the models' performances is conducted based on the specifics of the applications.