This paper describes our approaches to solving the MentalRiskES task, which belongs to the IberLEF (Iberian Languages Evaluation Forum) shared task. The task aims to identify the eating disorders and depression of a user using a series of Telegram messages. Our proposed system uses the traditional TFiDF method to represent the messages and then utilizes these representations as input for machine learning models. The best results for classification were obtained using the Naive Bayes classifier, while in the regression task, the best models were Gradient Boots Regressor and Linear Regressor. Despite its simplicity, we demonstrated that our traditional approaches can still achieve competitive results in recent NLP tasks, obtaining the best results in the case of detecting depression and eating disorders.