# Applications of Machine Learning Algorithms in Geriatrics
## 3 ML Models Used in the Diagnosis and Monitoring of
### Classification of the Studied Models Based on Objectives  
ML algorithms are classified into prediction algorithms and classification algorithms. In Table 2, the classification of works in the field of geriatrics was carried out based on the objective of the ML algorithm. Thus, nine papers have classification as their main objective, while seven papers have prediction as their main objective. Prediction works aim to anticipate various scenarios, while classification algorithms label a certain scenario as belonging to a specific group. The fundamental difference between classification algorithms and clustering algorithms lies in the fact that, in the case of classification, there are labels for the training dataset, while, in the latter case, clustering elements are actually used. However, most of the works are disseminated on the two mentioned objectives.  
Classification models are used in the medical field to associate a label with a patient's diagnosis. A concrete example is the presence or absence of a bacillus or exceeding a safety threshold of a certain vitamin dose in the blood. These classification models are used in differential diagnosis or screening in the hospital or community setting. Table 3 outlines that RF is the most frequently used classification model in the specialized literature. Researchers have analyzed multiple classification models in relation to the geriatric field, but only some of these models are effective in classification. From Table 3, it can be noted that the RF and SVM models are the most used.  
Regarding prediction models, they estimate a future event, such as postoperative mortality, length of stay, recurrence of falls, or progression of a specific disease. These models are used in the medical field for making anticipated clinical decisions. They are also used for the allocation of medical resources. These aspects are summarized in Table 4, where  
\begin{table}
\begin{tabular}{c c} \hline
**ML Algorithm Objective** & **References** \\ \hline Classification & [5, 11, 18, 28, 32, 44, 51, 55, 57] \\ Prediction & [14, 24, 26, 30, 40, 54, 58] \\ \hline \end{tabular}
\end{table}
Table 2: Classification of literature papers based on the ML algorithm objective.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **References** \\ \hline RF & [32, 44, 51, 55, 57] \\ SVM & [5, 11, 28, 44] \\ NB & [18] \\ KNN & [28] \\ DT & [44] \\ LR & [44] \\ XGBoost & [18] \\ MLP & [44] \\ \hline \end{tabular} Note: MLP—Multilayer Perceptron.  
\end{table}
Table 3: Classification models investigated in the literature for geriatric purposes.  
it is observed that XGBoost is the most used in prediction problems, such as osteoporosis prediction, cognitive deterioration, or postoperative delirium. Additionally, RF and LR are examined in predictions regarding postoperative mortality.  
Therefore, classification models and prediction models are researched in the field of geriatric medicine with different objectives. Classification models are used to diagnose and label the clinical conditions of patients. Prediction models are used to anticipate, forecast, predict, and prevent the progression of the patient's condition, and plan personalized geriatric treatment. In both cases, RF and XGBoost are considered the most popular models.