Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan

This project aims to develop a web-based application utilizing the Random Forest Classification Algorithm to aid concerned parents in detecting potential Autism Spectrum Disorder (ASD) symptoms in their children aged 1-6 years in Malaysia. The application considers various factors, including childre...

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Main Author:	Shahron Nizan, Muhammad Shahmi
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Medical technology
Online Access:	https://ir.uitm.edu.my/id/eprint/89039/1/89039.pdf
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id	my-uitm-ir.89039
record_format	uketd_dc
spelling	my-uitm-ir.890392024-03-19T07:08:32Z Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan 2023 Shahron Nizan, Muhammad Shahmi Medical technology This project aims to develop a web-based application utilizing the Random Forest Classification Algorithm to aid concerned parents in detecting potential Autism Spectrum Disorder (ASD) symptoms in their children aged 1-6 years in Malaysia. The application considers various factors, including children's gender, parental income range, and responses to the Modified Checklist for Autism in Toddlers (M-CHAT) questionnaire, to provide risk categorization and recommend nearby support facilities. By offering an online platform, the project addresses the increasing prevalence of ASD and helps parents seek professional support for their children. It also assists parents in preparing their ASD-affected children for primary school by suggesting appropriate assistance options like Program Pemulihan Dalam Komuniti (PDK), Program Pendidikan Khas Integrasi (PPKI), and Program Pendidikan Inklusif (PPI). The project follows a modified waterfall approach, focusing on creating a user-friendly interface and integrating the Random Forest Classification Algorithm for accurate detection. The results show the algorithm's impressive performance with an 86% precision in predicting ASD traits. In conclusion, this web-based application provides a reliable and accessible tool for early ASD detection, empowering parents to assess their children's risk and seek appropriate support. However, the project acknowledges limitations such as a small dataset and subjective questionnaire-based assessments, calling for further attention. Future work involves data expansion techniques, integrating objective measures alongside questionnaires, and collaborating with relevant organizations to enhance the system's capabilities and effectiveness in detecting ASD in Malaysian children. 2023 Thesis https://ir.uitm.edu.my/id/eprint/89039/ https://ir.uitm.edu.my/id/eprint/89039/1/89039.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Medical technology
spellingShingle	Medical technology Shahron Nizan, Muhammad Shahmi Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan
description	This project aims to develop a web-based application utilizing the Random Forest Classification Algorithm to aid concerned parents in detecting potential Autism Spectrum Disorder (ASD) symptoms in their children aged 1-6 years in Malaysia. The application considers various factors, including children's gender, parental income range, and responses to the Modified Checklist for Autism in Toddlers (M-CHAT) questionnaire, to provide risk categorization and recommend nearby support facilities. By offering an online platform, the project addresses the increasing prevalence of ASD and helps parents seek professional support for their children. It also assists parents in preparing their ASD-affected children for primary school by suggesting appropriate assistance options like Program Pemulihan Dalam Komuniti (PDK), Program Pendidikan Khas Integrasi (PPKI), and Program Pendidikan Inklusif (PPI). The project follows a modified waterfall approach, focusing on creating a user-friendly interface and integrating the Random Forest Classification Algorithm for accurate detection. The results show the algorithm's impressive performance with an 86% precision in predicting ASD traits. In conclusion, this web-based application provides a reliable and accessible tool for early ASD detection, empowering parents to assess their children's risk and seek appropriate support. However, the project acknowledges limitations such as a small dataset and subjective questionnaire-based assessments, calling for further attention. Future work involves data expansion techniques, integrating objective measures alongside questionnaires, and collaborating with relevant organizations to enhance the system's capabilities and effectiveness in detecting ASD in Malaysian children.
format	Thesis
qualification_level	Bachelor degree
author	Shahron Nizan, Muhammad Shahmi
author_facet	Shahron Nizan, Muhammad Shahmi
author_sort	Shahron Nizan, Muhammad Shahmi
title	Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan
title_short	Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan
title_full	Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan
title_fullStr	Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan
title_full_unstemmed	Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan
title_sort	early autism spectrum disorder detection using machine learning / muhammad shahmi shahron nizan
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89039/1/89039.pdf
_version_	1794192192166690816

Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan

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