Early Autism Disorder Prediction Using Machine Learning

Ahmad Ridlan, Muhaimin Hasanudin, Ofelia Cizela da Costa Tavares, Daniel Eliazar Latumaerissa

Abstract


Autism is a behavioural disorder caused by neurodevelopmental disorders in the brain. This condition makes it difficult to communicate, socialize, and learn. One of the machine learning algorithms used for early autism prediction is the C4.5 algorithm, which considers the importance of attributes in effectively dividing data. Linear regression is used to predict early autism and identify associations between certain attributes and the likelihood of autism. The aim of this research is to develop a predictive model for early detection of autism using the C4.5 algorithm and linear regression. Additionally, the research aims to address the need for accurate and timely predictions of autism, as the long and expensive diagnostic process causes delays in intervention. This research emphasizes the importance of early detection in the child's growth and development process for rapid intervention and treatment to improve the quality of life for individuals with autism. The dataset from the Machine Learning Repository consists of 1122 data instances with 20 attributes. The results show that the C4.5 algorithm achieved the highest accuracy of 94%, while the linear regression algorithm achieved 44%. These findings suggest that the C4.5 algorithm is more effective in predicting autism than linear regression.

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References


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