Authors:
Caroline Jandre
1
;
Bruno Santos
1
;
Marcelo Balbino
1
;
Débora de Miranda
2
;
Luis Zárate
1
and
Cristiane Nobre
1
Affiliations:
1
Department of Computer Science, Pontifical Catholic University of Minas Gerais, Minas Gerais, Brazil
;
2
Department of Pediatrics, Federal University of Minas Gerais, Minas Gerais, Brazil
Keyword(s):
Dimensionality Reduction, Features Selection, Machine Learning, ADHD, School Performance.
Abstract:
Attention-Deficit/Hyperactivity Disorder (ADHD) is defined by harmful inattention, disorganization, and/or hyperactivity and impulsivity. ADHD can negatively affect an individual’s life, but it is not a definitive factor for poor school performance. This work aims to identify classification rules that best describe the school performance in arithmetic, writing, and reading of students with ADHD. For this, information obtained from the Genetic Algorithm, Random Forest and specialists in ADHD were used so that later the VTJ48 and JRip algorithms could be applied. It is usual in the health area to collect various information about the individual, resulting in the frequent need to reduce the base’s dimensionality. The results found were promising, reaching up to 92% of F-Measure. The discovered rules point to environmental and emotional factors as drivers of school performance prognosis and reinforce that ADHD is not synonymous with academic failure.