Implementation of Machine Learning and Deep Learning Models Based on Structural MRI for Identification of Autism Spectrum Disorder

Saputra, Dimas Chaerul Ekty and Maulana, Yusuf and Win, Thinzar Aung and Phann, Raskmey and Caesarendra, Wahyu (2023) Implementation of Machine Learning and Deep Learning Models Based on Structural MRI for Identification of Autism Spectrum Disorder. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 9 (2). pp. 307-318.

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Abstract

Autism spectrum disorder (ASD) is a developmental disability resulting from neurological disparities. People with ASD frequently struggle with communication, social interaction, and limited or repetitive interests or behaviors. People with ASD may also have unique learning, movement, and attention styles. People living with ASD can be interpreted as 1 in every 100 individuals in the globe having ASD. The abilities and requirements of autistic individuals vary and may change over time. Some autistic individuals can live independently, while others have severe disabilities and require lifelong care and support. Autism frequently interferes with educational and employment opportunities. Additionally, the demands placed on families providing care and assistance can be substantial. Important determinants of the quality of life for persons with autism are the community's attitudes and the level of support provided by local and national authorities. Autism is frequently not diagnosed until adolescence, even though autistic traits are detectable in early infancy. This study will discuss the identification of Autism Spectrum Disorders using Magnetic Resonance Imaging (MRI). MRI images of ASD patients and MRI images of patients without ASD were compared. By employing multiple machine learning and deep learning techniques, such as random forests, support vector machines, and convolutional neural networks, the random forest method achieves the utmost accuracy with 100% using a confusion matrix. Therefore, this technique can optimally identify ASD through MRI.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	12 May 2023 01:31
Last Modified:	12 May 2023 01:31
URI:	http://eprints.uad.ac.id/id/eprint/43131

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