Genetic Algorithms for Feature Selection in the Children and Adolescents Depression Context

Abstract

Depression is the leading cause of disability in the world and, according to the World Health Organization, is the leading cause of illness and disability in the adolescence. Previous research has found that half of all people who developed mental disorders had their first symptoms up to age 14. However, what are the most important characteristics to determinate whether or not someone has depression? Genetic algorithms have been used quite efficiently in the problem of feature selection from a dataset. Thus, the main objective of this work is to use genetic algorithms to search for the most relevant features to improve the performance of classifiers to assist in the diagnosis of depression in children and adolescents. The dataset used in this work contains information on 166 children and adolescents between 10 and 16 years of age, of whom 67 are males and 99 are females with different depressive symptoms. The genetic algorithm found, in its best result, a set of 55 features out of the 112 in the dataset. The feature referring to how one feels with her/his own appearance was the most used in the solutions returned by the genetic algorithms. The use of the dataset composed of the 55 features selected by the genetic algorithms improved the performance of the classifiers by 6 to 20 percentage points, reaching f-measure values between 90% and 96%, which is a very significant result in the depression diagnosis context.