Abstract

Planning high-performance sports training involves making decisions under uncertainty. There are no deterministic algorithms that allow handling the complexity of biological systems and individual variability in the construction of plans. For this reason, in this study, artificial intelligence techniques are applied to the construction of personalized training plans. In the methods section, the CACIA model is presented to construct training plans that combine linguistic data summarization techniques with elements of neutrosophic theory. The variables considered by the proposed model were anthropometric indicators, biorhythms, nutrition, and psychological factors. Then, in the results section, the proposal is validated based on an analysis of the performances of athletes at the national championships. The results were compared between the control and experimental groups using non-parametric tests and the SPSS tool. It was found that the CACIA model significantly improved the results of the experimental group compared with the control group. In this study, the use of linguistic summarization of the data allowed the creation of linguistic summaries that were used in the adaptation and improvement of the constructed plans.