# 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
[22] evaluate vitamin D status in geriatrics through multivariable logistic regression (LR), achieving an area under the curve (AUC) of 62.5%. The same tests are performed to identify procalcitonin (PCT) levels in geriatric patients with a preliminary diagnosis of sepsis upon admission to intensive care [23]. The level of vitamin D3 is investigated by [PERSON] et al. [24], and the XGBoost algorithm is reported to have the best results in variable selection and assessing its importance on the health of the elderly. One of the features of AI tools aims to assist medical staff in making the best decisions. [PERSON] et al. [25] evaluate the impact of AI tools in geriatric medical assistance for the analysis of patients' chronic diseases. The authors note RF as the most used for this purpose. [PERSON] et al. [26] predict the risk of falls in hospitalized elderly patients by combining data from electronic medical records with the geriatric assessment. In this paper, several models were analyzed, but XGBoost had the best performance, reporting an accuracy of 73.2%, a sensitivity of 91%, and a specificity of 27%. [PERSON] et al. [27] develop ML models to predict the length of hospital stay for geriatric patients with hip fractures. Additionally, the paper aims to identify associated risk factors. Among the inspected models, DT achieved an accuracy of 92.4% and an AUC of 98.8%. [PERSON] et al. [28] developed a fall detection system for the elderly using an accelerometer. The paper investigates time series, and the data are trained by specific ML algorithms. The best results are reported by RF, which achieved an accuracy of 99%, as well as by LR, which also achieved 99%. Furthermore, [PERSON] and [PERSON] [29] designed a wearable bracelet that monitors the health of elderly people. In the case of hip fractures in geriatric patients, [PERSON] et al. [30] developed a prediction model regarding mortality, which was investigated using the RF algorithm. In the paper by [PERSON] et al. [31], a model for classifying the level of frailty in the elderly is developed. Among the inspected algorithms, the Support Vector Classifier (SVC) and Multilayer Perceptron achieved the best results, with a median of 95%. Moreover, on the same subject, [PERSON] et al. [32] analyze various frailties of the elderly using portable sensors with a focus on gait analysis. Among the models examined, the k-Nearest Neighbors (KNNs) model achieved the best results, with an accuracy of 99.4%. In the paper by [PERSON] and [PERSON] [33], the implications of medical assistance through IoT devices with wearable sensors are presented. The paper analyzes data from these devices using threshold-based algorithms and identifies the SVM and KNN algorithms as having a sensitivity between 90% and 100%. The best values in terms of accuracy are obtained for the Convolutional Neural Network (CNN) and KNN. A specificity with values between 83 and 100% was reported for the RF and SVM algorithms. Geriatric health in the community can be investigated based on factors associated with pre-frailty in middle-aged adults, using ML algorithms, for which [PERSON] et al. [34] achieved a maximum AUC of 81.7% and an accuracy of up to 78.7%. [PERSON] et al. [35] monitor geriatric health through wearable sensors, and the data is analyzed using a multitude of algorithms, achieving an accuracy of 97.5% for RF in the best scenario. A special class of applications that use ML models is those in the neurological field [36]. [PERSON] et al. [37] predict the presence of intracranial hematoma in elderly patients with traumatic brain injury. The best algorithm was Naive Bayes (NB), which achieved an Area under the Receiver Operating Characteristic Curve (AUC-ROC) of 84.6%, an F1-score of 92%, and a sensitivity of 95%. [PERSON] et al. [38] identify operative factors in the diagnosis of hepatocellular carcinoma undergoing hepatectomy. Among the analyzed algorithms, classification algorithms are the most suitable for predicting patient survival rates.