Cicek G.Akan A.2024-03-132024-03-1320239798350328967https://doi.org/10.1109/TIPTEKNO59875.2023.10359235https://hdl.handle.net/20.500.12662/28592023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- -- 195703Attention deficit hyperactivity (ADHD) is a psychiatric disorder that affects millions of children and many times last into adulthood. There is no single test that can show whether a person has ADHD. The symptoms of ADHD vary from person to person. Therefore it is hard to diagnose ADHD contrary many physical illness. Our aim is to create methods to minimize human effort and increase accurate of diagnosis of attention deficit hyperactivity disorder. So, we collected Structural Magnetic Resonance Imaging (MRI) from 26 subjects: 11 controls and 15 children diagnosed with ADHD. The data was provided from NPIstanbul NeuroPsyhiatric Hospital. We used k-means clustering algorithm to extract gray matter and white matter from the axial plane. Four features is extracted from these region; area of gray matter, area of white matter and perimeter of gray matter, perimeter of white matter. The most important attribute was determined by using principal component analysis. The models were built on the k-nearest neighbors algorithm (knn) and decision tree using Matlab machine learning toolbox. The experiments were conducted on a full training dataset including 26 instance and 5 fold cross validation was adopted for randomly sampling training and test set. The outcome of our study will reduce the number medical errors by informing physicians in their determination of diagnosing of attention deficit hyperactivity disorder. These method we used classifies ADHD successfully up to % 91 accuracy. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessADHDClassificationdecision treegray and white matterk-nearest neighborConference Object10.1109/TIPTEKNO59875.2023.103592352-s2.0-85182739679N/A