自闭症谱系障碍
机器学习
人工智能
支持向量机
MATLAB语言
计算机科学
自闭症
神经影像学
随机森林
心理学
算法
发展心理学
精神科
操作系统
作者
Hindumathi Voruganti,Ameya Endla,Bhople Vaibhavi,Kavya Sri Vatadi,Jennifer Chalichemala
出处
期刊:Practice, progress, and proficiency in sustainability
日期:2024-01-05
卷期号:: 246-261
标识
DOI:10.4018/979-8-3693-1186-8.ch014
摘要
Early infancy may see the onset of symptoms, but it may take several visits to a pediatrician before a diagnosis is made. Long questionnaires, skilled medical experts, and occupational therapists are needed for subjective diagnosis of Autism spectrum disorder (ASD), which is a combination of developmental defects that causes social and behavioral deficits. This proposed work focuses on analyzing multiple machine learning models, constructed using MATLAB, to identify ASD using the ABIDE (Autism brain imaging data exchange) dataset, such as random forest, k nearest neighbors (K-NN), support vector machine (SVM), and decision trees. A considerable number of ASD patients and non-ASD controls were collected from 17 research and clinical institutes throughout the world as part of the global cooperative project ABIDE to create the dataset known as ABIDE. The main benefit of this mechanism is that it may quickly and objectively replace time-consuming exams now utilized in practice by utilizing recent advancements in machine learning and neuroimaging techniques.
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