A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA

A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA

Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. ASD prediction is difficult because the diagnostic factors may not be based solely on observation. The project focuses on using ASD screenin...

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Main Author: Yeap, Ming Yue
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://ir.unimas.my/id/eprint/44213/1/Yeap%20Ming%20Yue%20%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/44213/2/Yeap%20Ming%20Yue%20%20ft.pdf
http://ir.unimas.my/id/eprint/44213/
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Institution: Universiti Malaysia Sarawak
Language: English
English
id my.unimas.ir.44213
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spelling my.unimas.ir.442132024-01-18T04:08:13Z http://ir.unimas.my/id/eprint/44213/ A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA Yeap, Ming Yue QA75 Electronic computers. Computer science Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. ASD prediction is difficult because the diagnostic factors may not be based solely on observation. The project focuses on using ASD screening data to predict ASD traits in adults. This project aims to predict ASD traits in adults based on screening data using a machine learning approach. This can help them decide whether to seek a medical practitioner. The project proposed using classification, which is one of the machine learning approaches to predict autism spectrum disorder. The proposed prediction models are Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours, Naïve Bayes, and Neural Network. The methodology adopted by the project is knowledge discovery in databases (KDD) to accomplish the needs of this project. The steps include domain understanding, data selection, data pre-processing, data transformation, data mining/modelling and model evaluation. The project will create a dataset based on AQ-10 adults questionnaire data that will facilitate future work in future work in predicting ASD in adults. Feature selection will be performed to find useful features in predicting ASD traits in adults. The performance of the classification models for ASD will be compared. Finally, the best classification model for ASD prediction was a model trained using the Support Vector Machine (SVM) algorithm Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44213/1/Yeap%20Ming%20Yue%20%20%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/44213/2/Yeap%20Ming%20Yue%20%20ft.pdf Yeap, Ming Yue (2023) A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Yeap, Ming Yue
A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA
description Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. ASD prediction is difficult because the diagnostic factors may not be based solely on observation. The project focuses on using ASD screening data to predict ASD traits in adults. This project aims to predict ASD traits in adults based on screening data using a machine learning approach. This can help them decide whether to seek a medical practitioner. The project proposed using classification, which is one of the machine learning approaches to predict autism spectrum disorder. The proposed prediction models are Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours, Naïve Bayes, and Neural Network. The methodology adopted by the project is knowledge discovery in databases (KDD) to accomplish the needs of this project. The steps include domain understanding, data selection, data pre-processing, data transformation, data mining/modelling and model evaluation. The project will create a dataset based on AQ-10 adults questionnaire data that will facilitate future work in future work in predicting ASD in adults. Feature selection will be performed to find useful features in predicting ASD traits in adults. The performance of the classification models for ASD will be compared. Finally, the best classification model for ASD prediction was a model trained using the Support Vector Machine (SVM) algorithm
format Final Year Project Report
author Yeap, Ming Yue
author_facet Yeap, Ming Yue
author_sort Yeap, Ming Yue
title A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA
title_short A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA
title_full A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA
title_fullStr A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA
title_full_unstemmed A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA
title_sort comparative study of machine learning models for prediction of autism spectrum disorder using screening data
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44213/1/Yeap%20Ming%20Yue%20%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/44213/2/Yeap%20Ming%20Yue%20%20ft.pdf
http://ir.unimas.my/id/eprint/44213/
_version_ 1789430375720681472

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