Temporal Normalization and Brain Image Analysis for Early‐Stage Prediction of Attention Deficit Hyperactivity Disorder (ADHD)

A neurological condition known as attention deficit hyperactivity disorder (ADHD) impacts a person's behavior. Concern has been intrigued by the growing rates of ADHD in kids and teenagers throughout the world, which calls for strategies for early detection and diagnosis. This chapter aims to analyze the different machine learning algorithms on the ADHD dataset to serve as a proof of concept of the feasibility of using machine learning models to produce a medically viable solution for the early detection of ADHD. It also discusses the Exploratory Data Analysis methods on the ADHD-200 dataset as it provides a better overview of data by quickly analyzing and generating detailed reports of the dataset, saving both time and effort. The importance of temporal normalization on 4D NIfTI data and how it affects statistical assumptions, confounding factors, and comparability. Preprocessing, feature extraction, statistical analysis, cross-validation, and interpretation/validation are some of the phases in this study of brain images for the identification of ADHD. The analyzed K-nearest neighbors (accuracy: 0.92), random forest (score: 0.77), and linear regression (score: 0.765) algorithms show promising possibilities for analyzing the presented data. This study adds an understanding of ADHD and supports programs like the ADHD-200 Sample, which encourages open data sharing to increase this field's scientific expertise.