Analysis of attention deficit hyperactivity disorder using various classifiers
subject: ADHD; Bayes net; Classification; Data mining; J48 decision tree; Logistic model tree; Nae bayes; Random forest; Sequential minimal optimization
description: Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral childhood impairment that wipes away the beauty of the individual from a very young age. Data mining classification techniques which are becoming a very important field in every sector play a vital role in the analysis and identification of these disorders. The objective of this paper is to analyze and evaluate ADHD by applying different classifiers like Nae Bayes, Bayes Net, Sequential Minimal Optimization, J48 decision tree, Random Forest, and Logistic Model Tree. The dataset employed in this paper is the first publicly obtainable dataset ADHD-200 and the instances of the dataset are classified into low, moderate, and high ADHD. The analysis of the performance metrics and therefore the results show that the Random Forest classifier offers the highest accuracy on ADHD dataset compared to alternative classifiers. With the current need to provide proper evaluation and management of this hyperactive disorder, this research would create awareness about the influence of ADHD and can help ensure the proper and timely treatment of the affected ones. Springer Nature Singapore Pte Ltd 2021.
creator: George H.K.; Nizar Banu P.K.
source: Lecture Notes on Data Engineering and Communications Technologies, Vol-53, pp. 283-296.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2021-01-01
rights: Restricted Access
relation: ISSN: 23674512
format: Online
language: English
type: Book chapter
coverage: George H.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India