Classification Autism Severity Using a Multidimensional Behavioral Datasets

Machine learning analysis of Autism phenotype data supports a four-dimensional continuum with three overlapping subtypes

A Multi-Model Explainable Framework for Autism Spectrum Disorder Classification Through Questionnaire-Based Screening and Neuroimaging Analysis

Autism Spectrum Disorder (ASD) classification using machine learning has shown promising results on behavioral screening datasets, however, such performance may be influenced by embedded questionnaire scoring and threshold-based patterns. This study presents an explainable multi-model ASD classification Framework that consist of questionnaire based screening, augmentation analysis, rule based interpretation, explainable artificial intelligence, and rs-fMRI based neuroimaging classification. The proposed framework is organized into three progressive models. ASD classification on child behavioral dataset focuses on child behavioral screening data and evaluated Logistic Regression, XGBoost, Linear Support Vector Machine, and Bernoulli Naive Bayes with XAI and rule-based interpretation. Multi-cohort ASD classification framework combined child, adolescent, and and adult AQ_10 based datasets and assessed the same classifiers under real, SMOTE, ADASYN, CTGAN, and TVAE training conditions. ABIDE-1 multi-modal ASD classification extended the analysis to ABIDE-1 rs-fMRI functional connectivity and phenotypic features using ElasticNet Logistic Regression, Linear SVM, Ridge Classifier, and SGD LogLoss classifier. The questionnaire based models achieved strong performance, with ASD classification on child behavioral dataset framework obtaining a best accuracy of 97% using Linear SVM and Multi-cohort ASD classification framework reaching up to 100% accuracy in selected real and traditional oversampling settings for logistic Regression and linear SVM. SMOTE and ADASYN produced more stable performance than CTGAN and TVAE. ABIDE-1 multi-modal ASD classification framework achieved a best accuracy of 70% using ElasticNet Logistic Regression, reflecting the grater complexity of neuroimaging based ASD classification.

Autism Spectrum Disorder Identification using Machine Learning with Hybrid Feature Selection and Optuna Hyperparameter Optimization with SHAP Explainability

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.

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.

Classification of Evidence-Based Practices Learning Strategies in Autism Spectrum Disorder Using Random Forest

Selecting Evidence-Based Practices learning strategies for children with Autism Spectrum Disorder requires careful consideration of children’s diverse ability profiles. This study developed a classification model for Evidence-Based Practices learning strategies using the Random Forest algorithm based on a primary dataset consisting of 106 records. The features used include child age, gender, verbal ability, Autism Spectrum Disorder severity level, learning media, and language learning difficulties. The classification target consists of four classes: ABA, Visual Method, PECS, and Speech Therapy. Model evaluation was conducted using an 80:20 hold-out split, Stratified 5-Fold Cross Validation, Repeated Stratified Cross Validation, ablation test, and feature importance analysis. In the 80:20 hold-out scenario, Random Forest achieved an accuracy of 45.45% and an F1-Macro score of 0.4393. In Stratified 5-Fold Cross Validation, the model obtained an average accuracy of 38.61% ± 7.17% and an F1-Macro score of 0.3803 ± 0.0736. The repeated cross-validation results showed an average accuracy of 34.45% ± 9.46% and an F1-Macro score of 0.3332 ± 0.0967. These findings indicate that Random Forest is able to form an initial classification model; however, its performance remains low to moderate. The limitations of this study lie in the limited dataset size, the relatively small number of samples per class, and the absence of external validation; therefore, the model cannot yet be used as a final recommendation system. This study is positioned as a preliminary study to support the development of a Decision Support System for selecting Evidence-Based Practices learning strategies for children with Autism Spectrum Disorder.

Early detection of autism spectrum disorder through hybrid deep learning and classical machine learning approaches

These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool and will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.