Cristhian Ismae Gómez GaonaAndrea del Rocío Mejía RubioJosé Rubén León PérezRODRÍGUEZ FLORES, JESÚS ALBERTOJESÚS ALBERTORODRÍGUEZ FLORESZilma Diago AlfesLaura Esther Muñoz EscobarCristian IncaJimmy Yaguana TorresMarco Velasco Arellano2025-11-272025-11-272025-07-31https://doi.org/10.56294/saludcyt20251970Bipolar affective disorder is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity of the diagnosis of bipolar affective disorder due to the overlapping of its symptoms with other mood disorders led researchers and doctors to search for new and advanced techniques for the precise detection of bipolar affection disorder. One of these methods is the use of advanced machine learning algorithms under a statistical methodology for building logistical regression models, Random Forest. Support vector machines, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, with 146 data collected from the psychiatric services affiliated with the mental health system of Ecuador. At the inferential level, the results suggest that the implementation of automatic algorithms based on the different methodologies for building models enables the successful prediction or classification of individuals with bipolar affective disorders in Ecuador compared to controlled patients who do not profile under this pathological picture. It is the best Random Forest statistical model (89.35 %) that dictates the best performance metrics compared to the Gradient Boosting model. The evolution of the overall prevalence of bipolar affective disorders in Ecuador over the past 22 years has increased by a small differential. However, from 2020 to 2022, there has been a considerable increase in the percentage prevalence of cases of bipolar affective disorders in Ecuador.enAlgorithmsDecision Tree Gradient BoostingMachine LearningRandom Forestjournal-article