Affective Computing is an interdisciplinary area that takes into account the emotions of users in the development of hardware and software. In current systems, identifying the emotional state of users can be important to meet preferences, needs and interactions through the development of flexible and adaptable interfaces, for example. For this, emotion mining is used in order to discover emotional patterns in human-computer interaction. The main objective of this work was to detect and classify 6 basic emotions (anger, fear, disgust, joy, sadness and surprise), from images of female and male facial expressions, obtained from public databases. To mine emotions, the supervised learning technique called Support Vector Machine (SVM) was used, where global results of 89.83% for accuracy and 92% for precision were obtained.

Currently, one of the leading causes of death around the world are caused by diseases or acute syndromes installed in the cardiovascular system of the human body. Thus, this paper presents a modern alternative for the detection of cardiovascular diseases from health indicators such as age, gender, glucose and cholesterol indices, used as inputs for machine learning systems. The evaluation is made by using supervised learning algorithms, such as K-Nearest Neighbours, Decision Tree, Logistic Regression, Voting Classification, from the accuracy observed during the testing period, in order to conclude what is the best alternative for the construction of an effective cardiovascular event predictor in the clinical routine.