Machine Learning Approach Applied to the Prevalence Analysis of ADHD Symptoms in Young Adults of Barranquilla, Colombia

Abstract

Disorder Attention Deficit/Hyperactivity Disorder, or ADHD, is recognized as one of the pathologies of high prevalence in children and adolescents from the global environment population; this disorder generates visible symptoms usually diminish with the passage of time in adulthood, however they remain concealed by demonstrations damnifican personal stability and human development apt. This article shows the results of the research aimed at determining the prevalence of symptoms of attention deficit hyperactivity disorder in Young Adults University of Barranquilla and its Metropolitan Area. The sample of 1600 young adults between 18 and 25 years, which has been estimated at 95% confidence level and a margin of error of 2.44%. The information was acquired through the application of exploratory instruments symptoms of attention deficit hyperactivity disorder. With the application of the algorithm different machine learning algorithms such as: Bagging, MultiBoostAB, Deci-sionStump, LogitBoost, FT, J48Graft, a high performance in the Bagging algorithm could be identified with the following results in quality metrics: Accuracy 91.67%, Precision 94.12%, Recall 88.89% and F-measure 91.43%.

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

“…As for the Beta band, which is associated with intense brain activity, active thinking, and focus [41], the connections in the frontal and cognitive electrodes provided insight into the attention the HC subject had during the target stimulus, which we did not observe in the MCI subject (Figure 7c,d). Figure 7 focuses on the brain connectivity of MCI and HC subjects.…”

“…In the context of cognitive impairment, we used graph theory to study how neuronal network changes affect information flow, leading to symptoms such as memory loss or difficulty with decision-making [45]. Previous studies have shown that subjects with AD and MCI have decreased BC in specific brain regions [41], suggesting that these channels (respective regions) may play a role in the disease and that changes in their connectivity could contribute to cognitive impairment; however, more research is required to understand the relationship between graph theory, centrality measures, and cognitive impairment.…”

“…We examined the connectivity matrices of both healthy control (HC) and mild cognitive impairment (MCI) subjects, utilizing the weighted Phase Lag Index (wPLI). These distinctive graph matrices were integrated into the text classification process, and we ap- As for the Beta band, which is associated with intense brain activity, active thinking, and focus [41], the connections in the frontal and cognitive electrodes provided insight into the attention the HC subject had during the target stimulus, which we did not observe in the MCI subject (Figure 7c,d).…”

“…In LDA, we use Singular Value Decomposition (SVD), which is the most suitable option for most cases. We have extracted all available linear discriminant components [41].…”