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Diagnosis of ADHD using SVM algorithm
Tisha, Ramachandran V, Arulalan K.V,
Published in ACM Press
Attention Deficit Hyperactivity Disorder (ADHD) is a Disruptive Behaviour Disorder characterized by the presence of a set of chronic and impairing behaviour patterns that display abnormal levels of inattention, hyperactivity, or their combination. Since most individuals especially children display these behaviours from time to time, it is be difficult to differentiate behaviours that reflect ADHD from those that are a normal part of growing up which makes the diagnosis a tricky job. In this paper, we apply a well known artificial intelligence technique, the SVM algorithm, for the diagnosis of the disorder. The major advantage of using SVM is that it helps in controlling the complexity of the problem of diagnosing. There has not been much development or research on ADHD using SVM algorithm. Hence this is the first attempt at diagnosing the problems using the algorithm. To improve on the overall identification accuracy; we also make use of the GA-based, Feature Selection Algorithm. Genetic algorithms are known to give good solution to very complex problems. In conclusion, we expect that AI techniques like SVM will certainly play an essential role in future ADHD diagnosis applications. © 2010 ACM.
About the journal
JournalData powered by TypesetProceedings of the Third Annual ACM Bangalore Conference on - COMPUTE '10
PublisherData powered by TypesetACM Press
Open Access0