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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
https://ir.uitm.edu.my/id/eprint/89039/
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spelling my.uitm.ir.890392024-03-19T07:08:32Z https://ir.uitm.edu.my/id/eprint/89039/ Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan 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 NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/89039/1/89039.pdf Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan. (2023) Degree thesis, thesis, Universiti Teknologi MARA, Melaka. <http://terminalib.uitm.edu.my/89039.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
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
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
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/89039/1/89039.pdf
https://ir.uitm.edu.my/id/eprint/89039/
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score 13.211869

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