Resumen. Muchas muertes en el mundo suceden a consecuencia de enfermedades cardiovasculares. El método propuesto combina metaheuristícas-Algoritmos Genéticos (AG)-, y los clasificadores KNN y Naive Bayes. Las pruebas se realizaron a través de una base de datos del Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) [1]. Las metaheurísticas se implementan para mejorar el rendimiento de los clasificadores. Los resultados experimentales demuestran que se logra hasta un 94 % de precisión en la clasificación.

Alexithymia is a condition that partially or completely deprives you of the ability to identify and describe emotions, and to show affective connotations in the actions of an individual. This problem has been taken to different research projects that seek to study its characteristics, forms of prevention, and implications, and that try to determine a measurement for the experience of an individual with this construct as well as the responses they provide to certain stimuli. Other studies that were reviewed aimed to find a connection between the responses of subjects diagnosed with alexithymia when facing a dynamic of emotional facial expressions to recognize and their assigned grade based on the Toronto Alexithymia Scale (TAS), a metric frequently used to evaluate the presence or absence of alexithymia in an individual. In this work, a review of the different articles that study this connection, as well as articles that describe the state of the art of the implementation of artificial intelligence algorithms applied to the treatment or prevention of secondary alexithymia is presented.

Suicide is today one of the leading causes of death in the world. People at risk of suicide commonly leave messages around them, and that often includes social media. Therefore, early detection of suicidal risk is a fundamental prevention factor. At present, the search for suicide risk based on text analysis does not consider fundamental factors such as the analysis based on suicide notes. This research proposes a new text analysis for the detection of suicidal risk based on the study of suicide notes.

Suicide prevention is one of the great issues of the current era. Institutions such as the World Health Organization, have continued to search for all possible alternatives for early detection and timely prevention. Suicide rates have grown more and more in the world, and Mexico, although it is not the country with the most suicides, is one of the countries with the highest growth in recent years. At present, the use of social networks has generated great changes in the way we communicate. Expressing yourself through a social network begins to be more common than expressing ourselves to human beings. Several studies, which will be presented later, show that it is possible to determine from the content of social networks: cases of depression, risk of suicide, and other mental problems. The use of technological tools, such as Natural Language Processing, has served as an effective ally for the early detection of risks, such as abuse, bullying or even detecting emotional problems. The present research seeks to carry out an in-depth analysis in the state of the art of the application of Natural Language Processing as an ally for the detection of suicide risk from the analysis of texts for Mexican Spanish in Social Networks.

Resumen. En la presente investigación se realiza la simulación del comportamiento de un usuario de Twitter, a través de un sistema multiagente, obteniendo las características de la personalidad de sus seguidores, aplicando el test de personalidad de los cinco grandes y los clasificadores Naive Bayes y KNN.