TL;DR
XGBoost is the most suitable algorithm for clinical decision support in early ASD screening within the scope of this dataset, indicating strong generalizability.
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.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder which necessitates early and precise diagnosis to facilitate prompt intervention and enhance long-term results. Using behavioural and demographic data, this research offers a machine learningbased method for identifying ASD. The proposed system incorporates comprehensive data preprocessing techniques, such as addressing missing values, encoding categorical features, and feature scaling, to increase model efficiency. Additionally, the most pertinent characteristics that contribute to the classification of ASD are found using feature selection techniques. The predictive power of four machine learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF), Cat-Boost (CB), and Light Gradient Boosting Machine (LGBM) is assessed through implementation. Following training and validation using appropriate data partitioning methodologies, the model's performance is evaluated using different performance metrics. A comparative study is undertaken to find out how well every model represents complex trends in the dataset. According to the experimental results, boosting-based methods in particular, Cat-Boost and LGBM perform better because they can effectively handle categorical variables and minimise overfitting. Cat-Boost achieved best results with an accuracy of 85.14%. This study shows how cutting-edge machine learning methods can improve ASD screening and assist with data-driven clinical decision-making.
The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.
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.
A comprehensive machine learning framework to classify ASD severity (mild, moderate, severe) is developed and validates by investigating the differential impact of feature engineering and selection, revealing a critical “evaluation paradox” where radical, unguided feature reduction improved geometric cluster cohesion but degraded clinical accuracy.
ABSTRACT Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and inconsistencies in existing diagnostic tools. This review evaluates the role of machine learning and deep learning approaches in improving ASD prediction, with a particular focus on two important yet relatively underexplored methodological components: data augmentation and feature selection. A structured literature search was conducted across major scientific databases, including IEEE Xplore, PubMed, Scopus, and Google Scholar, to identify studies published between 2021 and 2024. The methodological quality and risk of bias of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool. A total of 26 peer‐reviewed studies were included based on their relevance to machine learning/deep learning‐based ASD prediction and their explicit application of data augmentation or feature selection techniques. Data augmentation methods were categorized into conventional approaches, such as geometric and color‐space transformations, and advanced techniques, including generative adversarial network‐based synthetic data generation. Although augmentation techniques may improve model robustness and help address dataset scarcity, relatively few studies conducted ablation analyses to isolate the contribution of individual augmentation strategies. Feature selection approaches were classified into filter, wrapper, and embedded methods. Commonly used techniques included information gain, chi‐square tests, recursive feature elimination, and elastic net regularization. While these methods may improve predictive performance and model interpretability, they are frequently applied without sufficient empirical justification or biological interpretation. Overall, this review highlights important methodological limitations, including limited external validation, insufficient ablation analyses, and inadequate evaluation frameworks, which reduce confidence in reported performance improvements and model generalizability. Future research should emphasize methodological transparency, robust validation strategies, multimodal data integration, and clinically interpretable modeling approaches to improve the reliability and clinical applicability of ASD prediction systems.
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