Machine Learning Based Early Detection of Autism Spectrum Disorder

Enhancing early detection of autism spectrum disorder through ensemble-based machine learning classifiers

The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process.

AUTDETECT: A Machine Learning-Based Web Tool for the Early Detection of Autism Spectrum Disorder in Children

Early diagnosis of ASD is crucial for timely intervention, especially in low-resource environments where access to specialized evaluation is limited. This study aimed to develop and validate a web-based application, supported by machine learning algorithms, to assist in the early detection of ASD using behavioral screening questionnaires. A responsive web application was designed using Django (backend) and React (frontend), deployed on Amazon Web Services. The system collects responses to the Q-CHAT-10 questionnaire from caregivers and uses multiple supervised learning models to predict ASD risk. The data used for model training and evaluation were obtained from a public ASD screening dataset. Data preprocessing, SMOTE for class balancing, and hyperparameter tuning through GridSearchCV were applied. Clinical validation was performed through pilot testing at a hospital in Lima, Peru. Among the tested models, the Support Vector Machine, Random Forest, and XGBoost classifiers achieved the highest performance, with F1-scores exceeding 0.90. The system showed a 80% reduction in processing time for the clinical evaluation process compared to the traditional workflow. Clinicians reported improved efficiency and usability, and the application demonstrated strong potential for scalable deployment in similar clinical settings. The proposed web-based system is a valuable tool for supporting early ASD detection in under-resourced environments, as its combination of validated screening tools and machine learning predictions enhances diagnostic workflows, enabling earlier intervention and better clinical decision-making.

Clinical Decision Support System for ASD Screening Using Ensemble Machine Learning

Advanced Computational Approaches for Early detection of Autism Spectrum Disorder using Machine and Deep Learning: Recent Trends and Perspective

Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.

Hybrid Intelligent Model for Prediction of Autism Spectrum Disorder in Children

A Hybrid Intelligent Model designed to predict ASD in pediatric cases, leveraging adaptive neuro-fuzzy systems integrates artificial neural network capabilities with fuzzy logic, offering a comprehensive approach to ASD prediction.

A Multi-Modal Approach for Early Autism Recognition Using Deep Learning and Sensory Data

This project introduces a smart, hybrid system that combines advanced deep learning technology with proven treatment methods, aiming to close the gap between diagnosis and meaningful help for autism, by blending advanced computational analysis with trusted treatment practices.