## 1 Introduction
### Related Works  
Machine learning techniques have been used to classify patients into Mild Cognitive Impairment (MCI), Cognitively Normal (CN) or dementia with the use of cognitive tests,such as the Clinical Dementia Rating--Sum of Boxes (CDR-SB) score, showcasing that some algorithms such as C4.5 and Naive Bayes performed well in this specific task of classification [2]. In another study, the development of a neural network model based on the Xception architecture was used to classify individuals and predict progression to MCI with the use of tensor-based image analysis. The results show very high classification accuracy, outperforming modern Convolutional Neural Network (CNN) models [3]. A systematic review also assessed methods in machine learning in combination with neuroimaging to improve post traumatic stress disorder diagnosis. Techniques like Support Vector Machines showed increased diagnostic accuracy in such applications [4]. Moreover, a systematic review from a group of researchers used a multimodal machine learning approach to compare structural brain changes in patients with depression and psychosis on specific parts of the brain. The results indicate potential diagnostic neuroanatomical patterns [5; 6]. The relationship between PTSD symptoms and brain functional connectivity was explored using a machine learning model that mapped MRIs from 50 patients. The model effectiveness during the final tests highlights the potential of machine learning applications in such scientific fields [7]. Regarding the early diagnosis of dementia, [PERSON] and [PERSON] developed a deep learning model with improved classification. To achieve this, they addressed dataset imbalance in order to achieve enhanced accuracy. The results showed significant improvement in evaluation metrics including accuracy, F1 score, recall and precision [8]. Another study proposed a machine learning approach for detecting dementia in early stages using neuroimaging features. Researchers developed a model that outperformed traditional benchmark models with impressively high accuracy, showcasing the effect the cognitive test scores have in prediction performance [9]. Another study proposes the use of a two-layer machine learning model for diagnosing the early stages of dementia. Based on Mini-Mental State Examination-KC (MMSE-KC) data, the model classifies the patients as normal or abnormal and then, based on mild MCI, differentiates further. In a variety of tested algorithms, the Multilayer Perceptron (MLP) approach showed exemplary accuracy for normal cases. On the other hand, Support Vector Machine (SVM) was the most effective for diagnosing MCI and dementia [10]. In a study by [PERSON], [PERSON] and [PERSON], a machine learning approach was used for detecting the early stages of dementia using demographic clinical data. The model successfully predicted dementia risk based on various factors such aging, quality of life and social interactions [11]. Another study used machine learning models such as Gradient Boosting and Random Forest based on demographic data, actigraphy and health data for the prediction of behavioral and psychological symptoms of dementia. Gradient boosting attained the highest accuracy, while Random Forest performed best in specific symptom predictions, indicating the utility of both models for clinical applications [12]. In a study, an estimation about dementia showed that 50 million people worldwide suffer from the disease, while caregivers are affected by erratic episodes of agitation. This study used a deep learning model to predict such episodes in advance with high percentages of accuracy [13]. A proof-of-concept study showed, using only one patient, how to utilize a multilayer perceptron model with common sensors to achieve 99% accuracy for detecting abnormal behaviors in dementia patients. Although further study is needed with bigger samples, it highlights the utilities of the Internet of Things in such applications in nursing homes [14].