A comparison of text representation approaches for early detection of anorexia

The excessive use of filters on photos, along with the hundreds of profiles on social networks that abuse retouching to reduce centimeters of their bodies, and the existing social pressure on body image, has caused an increase in Eating Disorders (ED). Thus, anorexia, bulimia nervosa and binge eating disorder are the main eating disorders that put physical and mental health at risk, especially among the very young people. Fortunately, there are technologies that allow the early detection of certain problems in different areas, in particular those related to safety and health, such as the mentioned above. Our main objective in this work is to analyze how different representations of texts behave for the early detection of people suffering from anorexia. Although we focus on ED, we believe that these results could be extended to other risks such as depression, gambling, etc. We employ k-TVT, an efficient and effective method used previously in the detection of signs of depression, as well as other more elaborated approaches such as Word2Vec, GloVe, and BERT. To compare the performance of these methods, we worked on a data collection provided by the eRisk 2018 laboratory to detect signs of anorexia disorder. Regarding the results, the performance of the different approaches was quite similar, with k-TVT and BERT being slightly better. We also conclude that k-TVT continues to be efficient with its flexibility, low dimension and easy computing being the more attractive characteristics.Workshop: WBDMD - Base de Datos y Minería de DatosRed de Universidades con Carreras en Informátic

Depression prediction using machine learning: a review

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases from 2016 to 2020 that use machine learning techniques to predict depression. The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. This understanding provides researchers with the fundamental components essential to predict depression. Fifteen articles were found relevant. We based our review on the Systematic Mapping Study (SMS) method. Three research questions were answered through this review. We discovered that sixteen variables were deemed important by the literature. Not all of the reviewed literature utilizes depression screening tools in the prediction process. Nevertheless, from the five screening tools discovered, the most frequently used were Hospital Anxiety and Depression Scale (HADS) and Hamilton Depression Rating Scale (HDRS) for general population, while for literature targeting older population Geriatric Depression Scale GDS was often employed. A total of twenty-two machine learning algorithms were identified employed to predict depression and Random Forest was found to be the most reliable algorithm across the publications

Memorias del Congreso Argentino en Ciencias de la Computación - CACIC 2021

Trabajos presentados en el XXVII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Salta los días 4 al 8 de octubre de 2021, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Universidad Nacional de Salta (UNSA).Red de Universidades con Carreras en Informátic