## 1 Introduction  
The continuous search for clinical diagnoses with greater accuracy has driven the use of hybrid models of computational technologies. These hybrid models use concepts of Artificial Intelligence (AI), Machine Learning (ML), and qualitative methods based on Verbal Decision Analysis (VDA), among others. The Machine Learning (ML) algorithms added agility to the diagnostic processes and have brought innovations to the field of health [1]. These hybrid models can be used to accelerate the diagnosis of neurological disorders such as cognitive and motor dementia in individuals infected with the Human Immunodeficiency Virus (HIV) and thus establish an earlier, more agile, and effective treatment [2]. HIV Associated Dementia (HAD) is the foremost source of morbidity and mortality in infected individuals and the most common in the younger population with HIV. Research has shown that age is not a prevalent factor for predisposition to dementia. Many individuals who are 50 years old or more became infected when they were much younger and have lived with the AIDS virus for an extended period asymptomatically without seeking professional help or the use of antiretroviral. When this age group has signs and symptoms of the disease, such as a cognitive deficit, which causes a negative impact on their quality of life and life expectancy, it causes great concern to health professionals and their relatives. The difficulty of an early and accurate diagnosis of dementia, especially among HIV-infected people, is due to the rarity of symptoms at the beginning of the infection, requiring a prolonged observation period to reach a diagnosis. Finding various likely events and characteristics to establish a diagnostic pattern for symptoms associated with dementia in HIV-infected individuals is a significant challenge; however, this would allow for early treatment [3]. The social relevance of this research is due to the advantages of using Information and Communication Technologies to predict the high risk of dementia in people with HIV. These technologies can point out the necessary components to assess problems through the identification of respective classified and ordered characteristics related to HAD. Furthermore, such technologies can be used to plan health strategies and referrals to specialists instead of neuropsychological tests, which are considered time consuming and inadequate for some patients. Although research into HIV has evolved over the last three decades, AIDS still has no cure. In addition, this disease is stigmatized in society, and as an aggravating factor, HIV-infected individuals are discriminated against in many situations, especially in work environments. Furthermore, society considers that competitiveness is a differential in terms of an individual's profile and productive capacity, and consequently, AIDS has become an exclusion factor in the process of wealth generation [4]. People living with HIV have an increased risk of cognitive impairment due to the neurotropism of the virus for the Central Nervous System (CNS), which is considered the second most affected site after the lymphatic system [5]. However, an early diagnosis points to promising horizons for these individuals. Early clinical interventions help control HIV in the body and allow these people to live longer and have a better quality of life [6]. The main characteristic of Machine Learning (ML) is its ability to learn through experience (historical data) and consequently improve its performance [7]. Machine Learning algorithms require consistent and accurate data to define whether a diagnosis is positive or negative. Decision support methods aim to assist in the decision-making process to simplify the analysis of a problem and its alternatives and thereby justify the choice of a particular action. A decision process using verbal methods analyzes the decisionmaker's preferences more reliably. The basis for decision making is the exposure of the pros and cons and their analysis [8].