Artificial Intelligence Techniques for Quality of Life Analysis: Systematic Mapping
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Introduction: Quality of life is a complex concept that encompasses multiple dimensions, from health and education to economic and social well-being. In recent years, the use of Artificial Intelligence (AI) has made it possible to analyze large volumes of data to better understand the factors that influence quality of life. However, although various applications have been developed in this field, there are still challenges in integrating different approaches and data sources.
Objective: This study analyzes how AI is being used in the assessment of quality of life. The main techniques used, the areas of application and the gaps in the literature are identified, in order to propose future lines of research.
Methods: A systematic review was conducted following Kitchenham's methodology, compiling studies from scientific databases such as Scopus, IEEE Xplore and Google Scholar. Inclusion and exclusion criteria, formulation of research questions, and other steps were applied to select relevant research on AI and quality of life.
Results: The most commonly used algorithms include Decision Trees (DT), Random Forest (RF), Neural Networks, and Support Vector Machines (SVM), applied in health, education, and socioeconomic conditions. However, most studies analyze factors in isolation, without integrating multiple dimensions.
Conclusions: Although AI is a promising tool for assessing quality of life, more integrative approaches need to be developed that combine diverse data sources and reflect the complexity of the concept.
- Artificial intelligence
- Deep Learning
- Machine Learning
- Systematic Mapping
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