## 2 Materials and Methods
### Proposed Hybrid Model Used in this Study  
There is great interest in applications for Artificial Intelligence (AI) in healthcare in order to be able to acquire an early diagnosis and early treatment. Furthermore, AI can work with large amounts of data and complex systems and generate relevant results. Health events are more stochastic and less deterministic; therefore, they need a centralized predictive control system to learn from their own experience, improving the same algorithms they use to provide recommendations [57]. The main characteristic of Machine Learning technology, a subset of AI, is its learning from experiences recorded in historical data, and its performance improves according to this learning [58]. In this study, a large amount of structured data that was composed of many rows and columns containing unique numbers or values was converted to extract only the information able to improve the proposed model, refined from several test bases, one for each learning cycle. The origin of each test base of each cycle counted on the exploration of a large and varied amount of data structured in rows and columns. The learning cycle had to be repeated until the results were satisfactory, consequently making the algorithm more accurate. The Dementia in People with HIV database was constructed using data collected from HIV-positive individuals aged fifty years and older with mental and clinical conditions suitable to participate in the database [59]. The data collection and monitoring took place at the outpatient clinic of the Reference Hospital for Infectious and Parasitic Diseases located in the city of Fortaleza, CE-Brazil. Data from 294 people were collected; unfortunately, due to the COVID-19 pandemic and the need for social isolation, no further increase in the sample was possible. For the training and testing basis, we applied k-fold cross-validation (with \(k=10\)), which performed the fitting procedure a total of ten times, with each fitting being performed on a training. The instruments used to acquire the data were: Evaluation of Clinical and Immunological Aspects; International HIV-Associated Dementia Scale; Brief Symptom Inventory; and Social Support and Instrumental Activities of Daily Living Scale. The individuals at high risk of dementia were identified after data collection. A total of 70% of the data was used for training and 30% for the tests. The collection was started after approval by the Research Ethics Committee of the Federal University of Ceara /Brazil, respecting the ethical precepts of nonmaleficence, benefcence, autonomy, and justice for its participants, under No. 3038967. The main objective of this study is to propose a hybrid model based on Machine Learning, associated with a multicriteria Verbal Decision Analysis method for the early diagnosis of dementia in HIV-infected individuals. The VDA method chosen was the ZAPROS-III-i, as presented in Section 2.2.4, due to its presence in several multicriteria decision support models in the health area. In turn, three fundamental Machine Learning algorithms were applied that, according to [60], have been widely successfully implemented in various areas, mainly in the health field, for classification and regression purposes. The Random Forest algorithm was presented in Section 2.2.1, the Logistic Regression was presented in Section 2.2.2, and the third Machine Learning algorithm, Naive Bayes, was presented in Section 2.2.3. This latter algorithm was included to serve as a third option to the Random Forest and Logistic Regression. The hybrid model proposed in the present study is shown in Figure 7, where the operational steps of the process can be seen. As can be seen in Figure 7, the process has four steps.