Harnessing Behavioural Insights for Autism Spectrum Disorder Prediction via Machine Learning
subject: autism spectrum disorder; machine learning; neural networks; random forest; stress management; support vector machines
description: This paper exploration has been done for predicting autism spectrum disorder or ASD while using certain machine learning classifiers. The multisource dataset used, covers behavioral, genetic, neuroimaging and sensor data. The classifiers used are: Random Forest, AdaBoost, Extra Trees, Logistic Regression, K Neighbors, and Bernoulli Naive Bayes. The results achieved are: The Bernoulli Naive Bayes (92.10%), Random Forest (91.53%) and Logistic Regression (91.34%). AdaBoost and Extra Trees performed well with accuracies 90.34% and 90.20%, respectively. K Neighbors Classifier had the lowest accurate outcome with 87.65%. This study explores improving ASD diagnosis, highlighting the effectiveness of various models and emphasizing the need for further research to address challenges such as model interpretability and data quality. 2025 IEEE.
creator: Rajak, Akash; Kumar, Amit; Singh, Shweta; Vidushi; Verma, Ankit; Mishra, Siddheshwari Dutt
source: 2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155421-7;
language: English
type: Conference paper
coverage: Rajak A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Kumar A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Singh S., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Vidushi, Christ (Deemed to be University), Bengaluru, India; Verma A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Mishra S.D., Mriirs, Faridabad, India