Lecture Notes in Education Psychology and Public Media
- The Open Access Proceedings Series for Conferences
Lecture Notes in Education Psychology and Public Media
Vol. 35, 03 January 2024
Open
Access | Article
Prediction of Autism Spectrum Disorder: Comparison and Tuning of Machine Learning Models
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Abstract
The early diagnosis in Autism Spectrum Disorder (ASD) is crucial for timely interventions to address the patients’ attentional and social challenges. The currently study aims to use machine learning algorithms to accurately predict ASD outcomes. Dataset from a Kaggle competition was used to perform the prediction analysis. Five supervised machine learning algorithms were employed: Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine Classifier (SVC), Random Forest (RF), and Decision Trees (DT). The models were fine-tuned using a range of possible hyperparameters and evaluated using ROC AUC scores. The best-performing model, Random Forest, achieved a training ROC AUC of 0.93. The model's performance in predicting the unseen test set resulted in a ROC AUC score of 0.8623. The outcome demonstrates the potentials of machine learning models in early prediction of ASD symptoms, which provides support for autistic individuals to enhance their quality of life and education.
Keywords
Autism Spectrum Disorder, supervised machine learning, classification, Random Forest
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Interdisciplinary Humanities and Communication Studies
- ISBN (Print)
- 978-1-83558-249-7
- ISBN (Online)
- 978-1-83558-250-3
- Published Date
- 03 January 2024
- Series
- Lecture Notes in Education Psychology and Public Media
- ISSN (Print)
- 2753-7048
- ISSN (Online)
- 2753-7056
- DOI
- 10.54254/2753-7048/35/20232015
- Copyright
- 03 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited