Machine-based learning system: Classification of ADHD and non-ADHD participants

Öztoprak, Hüseyin; Toycan, Mehmet; Kemal, Yaşar; Arıkan, Orhan; Doğutepe, Elvin; Karakaş, Sirel

doi:10.1109/siu.2017.7960704

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…Additionally, Deping Kuang and Lianghua He [ 42 ] utilised the deep belief network (DBN) with a magnetic resonance imaging (MRI) method to indicate ADHD, which achieved a classification accuracy of 85%. Likewise, Öztoprak et al [ 43 ] used the Disruptive Behaviour Disorders Rating Scale Form (DBDRS), for a study that employed a Decision Tree (DT) (CART), DT (CHAID), and neural network to yield prediction accuracies of 69.1%, 70.6%, and 61.8%, respectively. Bo Miao and Yulin Zhang Das [ 44 ] used the feature selection algorithm of three methods, for which accuracies of 77.92%, 80.52%, and 98.04% were obtained for the relief algorithm (Relief), verification accuracy (VA-Relief), and minimum redundancy maximum relevance (mRMR), respectively.…”

Section: Related Workmentioning

confidence: 99%

Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments

Chatpreecha

Usanavasin

2023

Children

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Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6–12 years) have been diagnosed with ADHD (2012–2018) This disorder is more likely to occur in males (12%) than females (4.2%). If ADHD goes untreated, there might be problems for individuals in the long run. This research aims to design a collaborative knowledge framework for personalised ADHD treatment recommendations. The first objective is to design a framework and develop a screening tool for doctors, parents, and teachers for observing and recording behavioural symptoms in ADHD children. This screening tool is a combination of doctor-verified criteria and the ADHD standardised screening tool (Vanderbilt). The second objective is to introduce practical algorithms for classifying ADHD types and recommending appropriate individual behavioural therapies and activities. We applied and compared four well-known machine-learning methods for classifying ADHD types. The four algorithms include Decision Tree, Naïve Bayes, neural network, and k-nearest neighbour. Based on this experiment, the Decision Tree algorithm yielded the highest average accuracy, which was 99.60%, with F1 scores equal to or greater than 97% for classifying each type of ADHD.

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Section: Related Workmentioning

confidence: 99%