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Overview of Machine Learning Methods in ADHD Prediction

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Advances in Bioengineering

Abstract

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects the social and personal traits of children between the age of 2 and 18, and the symptoms include inattentiveness and hyperactivity/impulsivity. Though this disorder is identified in childhood, it may persist till teenage in a few cases. ADHD is diagnosed on the basis of various rating scales that have been developed by experts. Additionally, MRI patterns are also used to study the anatomical and functional features of ADHD brain and the effect of medication. This chapter focuses on various machine learning models developed for accurate prediction of this disorder. Majority of machine learning studies were based on creating classification models, out of which SVM and ANN have been proved to give the most accurate diagnosis. A better predictive model with good correlation coefficient (CC) values, specificity and sensitivity has been generated with genetic programming-based algorithm. Numerous other relevant examples have also been cited in this chapter. The contents of the chapter will help the researchers to understand various techniques of ADHD prediction to provide better treatment for the children who are suffering from similar neurodevelopmental disorders.

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  1. School of Bioengineering Sciences & Research, MIT ADT University, Pune, India

    Navya Sethu & Renu Vyas

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  1. Navya Sethu
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  1. School of Bioengineering, MIT-ADT University, Pune, Maharashtra, India

    Renu Vyas

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Sethu, N., Vyas, R. (2020). Overview of Machine Learning Methods in ADHD Prediction. In: Vyas, R. (eds) Advances in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2063-1_3

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