# 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 clinical prediction models used for ML algorithms in the geriatric field have age, sex, comorbidities, and laboratory values, as well as preoperative and postoperative medications as input variables [4]. Walking speed is an indicator that classifies the elderly using ML algorithms. This characteristic suggests whether the patient belongs to a certain group, namely, young adults, healthy older adults, or geriatric patients without cognitive deficiencies [5]. The identification of biochemical, hematological, and immunological parameters in the blood provides an overview regarding the detection of malnutrition [6; 7]. Investigating these indicators provides an overview of the health status of geriatric versus non-geriatric patients [8]. In the category of these investigations, the dosage of vitamins in relation to the uniqueness of the patient is also included [9]. These investigations can be conducted using ML algorithms that personalize treatment based on the uniqueness of the patient [10]. [PERSON] et al. [11] note the Support Vector Machine (SVM) algorithm was the most studied, followed by Deep Learning (DL) algorithms, Decision Tree (DT), and other models. It is important to note the emphasis placed on SVMs in this paper, which is recorded as the most studied in the geriatric field. In the paper by [PERSON] and [PERSON] [12], large volumes of data, referred to as Big Data, are analyzed in relation to ML algorithms for the purpose of transforming geriatric anesthesiology tailored to the elderly population. The study was conducted on 797 patients and aimed to predict the risk of postoperative delirium. The model examined which method achieved the best accuracy, and it was Random Forest (RF) [13]. [PERSON] et al. [14] also investigated the prediction of postoperative delirium and identified SVMs as having the best results. [PERSON] and [PERSON] [15] compare the progression of chronic kidney disease and the associated complications between geriatric patients aged over 65 and non-geriatric patients aged under 65. In this study [15], statistical models such as the analysis of variance (ANOVA) were used to compare the study groups. These groupings can also be performed using ML algorithms specialized in unsupervised classification. Chronic kidney diseases are investigated by [PERSON] et al. [16] for elderly patients using geriatric assessment parameters. These investigations can be assisted by AI tools, even in kidney transplantation, which is studied by [PERSON] et al. [17] through a comparative analysis between elderly patients over 60 years old versus younger patients aged 18 to 59 years. In this paper, a  
Figure 2: Systematic structure of identified geriatric subdomains in the literature from the ML perspective. predictive model for post-transplant renal function is used, achieving an accuracy of 69.44%, which suggests the need for considerable improvements. Moreover, in the field of surgery, the degree of necessity for surgical intervention is predicted in patients over the age of 75. Thus, the emergency medical triage for geriatric patients with trauma is performed using the Extreme Gradient Boosting (XGBoost) algorithm, which achieved a prediction accuracy of 90.3%. Non-invasive medical diagnosis combines pulse measurements and frequency domain analysis to detect changes in the pulse wave [18; 19]. Using the statistical method ANOVA together with the U and Mann-Whitney tests, the concentrations of vitamin D25-OH in outpatient and hospitalized geriatric patients are compared [20]. The experiment does not aim to integrate ML methods, but the deficiency of vitamin D in the elderly could also be studied using them in future research. In the category of image processing for the purpose of unlabeled classification, using clustering algorithms, the work [21] was developed. This used the K-means algorithm for medical diagnosis using tongue images in various geriatric diseases. [PERSON] et al.