# Applications of Machine Learning Algorithms in Geriatrics
## 2 Methodology  
This study represents a systematic review of the literature, with the main aim of analyzing and synthesizing the applications of ML algorithms in the field of geriatrics. The scientific review is based on a research process structured in several stages. Initially, the ML models used in the diagnosis, monitoring, prevention, and treatment of geriatric patients are identified. Initially, the authors identify the ML algorithms used in the field of geriatrics and analyze them in their applications. The analysis targets the performance indicators identified at the level of each study. Subsequently, a search is conducted for all selected scientific articles from the WOS database, using a series of domain-specific keywords. The period analyzed spans from 1 January 2020 to 31 May 2025. In this way, the most recent contributions in the field are highlighted. Both scientific contributions and reviews were included in the analysis, as they provide an overview of the applications used in the geriatric field that are integrated with ML components. The inclusion criteria for the study were as follows: (1) the application of ML techniques for classification or prediction in the geriatric context, (2) the identification of the performance of the models used, (3) detailing the datasets used, and (4) the clinical characteristics analyzed. Theoretical works without practical involvement, as well as articles targeting age categories other than those specifically geriatric, were excluded. The selected works were analyzed both qualitatively,from the authors' perspective, through performance indicators, and quantitatively. For the qualitative analysis, the types of algorithms used, the clinical applicability domain, the objective pursued, and the datasets utilized were identified, all to create a technical synthesis regarding the performance indicators. For the qualitative analysis, the metrics reported in research articles were compared, with a focus on accuracy, sensitivity, and F1-score. The quantitative analysis is ensured through detailed searching in the WOS, PubMed, Scopus, and Institute of Electrical and Electronics Engineers (IEEE) Explore databases, according to the schematically structured logic represented in Figure 1. This diagram progressively illustrates the manner in which the Boolean expressions used in thematic searches were constructed. The four directions included the following:  
1. Generic terms represented by \"geriatr\"\", which will extract all research with derivatives of this expression. These terms aim to identify all studies in the literature on this topic within the 1 January 2020-31 May 2025 range. The search aims to identify the total number of papers, which will later be compared to the number specifically targeting ML components. This initial stage builds a general corpus related to GERIATRICS in the current scientific literature. 2. Subsequently, thematic filtering is applied to subdomains. The filters are DIET, NUTRITION, ELDERLY, and ML. These filters capture studies that address the interaction between nutrition and geriatric health, the elderly, and geriatrics, as well as ML and geriatrics. 3. The final search combines a common Boolean expression, the intersection of these components: geriatrics, diet, nutrition, elderly, and ML. The syntax is used to identify articles that trace the intersection of these four research development directions. The methodology considers a comprehensive approach by gradually combining the terms. In this way, comprehensive coverage of the literature is ensured. The other, the second role, is thematic focus through filtering based on technological terms, which allows for the extraction of works that explicitly address the application of ML in geriatrics, eliminating generalist literature. This query strategy builds an extraction of scientific data based on the distribution of ML algorithms in the subdomains of geriatrics.