Autism Spectrum Disorder Identification using Machine Learning with Hybrid Feature Selection and Optuna Hyperparameter Optimization with SHAP Explainability
Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 974-981· 0 citations· 11 references
Abstract
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.
A novel approach with an Optimized Pre-Trained Feature Selection With Support Vector Machine (OPFSVM) detection model for ASD is proposed to overcome the existing challenges and problems and highlight the implemented method's high effectiveness in early ASD detection and position it as an effective tool for timely and rapid analysis.
Robin Khurana, Satyaveer Singh· international journal of eng...· 0 citations
XGBoost is the most suitable algorithm for clinical decision support in early ASD screening within the scope of this dataset, indicating strong generalizability.
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.
Arazo Mohammed Mustafa· ARID International Journal f...· 0 citations
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine learning (ML) and deep learning (DL) have enabled the development of computational tools for ASD screening, classification, severity assessment, and intervention monitoring. This review synthesizes findings from 50 recent studies that applied ML and DL techniques to ASD-related datasets, including electroencephalography (EEG), eye-tracking, behavioral video, microbiome, voice acoustic, demographic, and multimodal data. The review addresses three key questions: (i) which data modalities and computational approaches are most frequently used, (ii) how diagnostic performance is evaluated across different study designs, and (iii) what methodological challenges limit clinical translation. The literature is organized according to data modality, algorithmic approach, and clinical readiness. Approaches examined include conventional ML methods, convolutional neural networks, graph neural networks, hybrid deep learning architectures, federated learning, explainable artificial intelligence, topological data analysis, and multimodal fusion. The findings suggest that multimodal and graph-based approaches provide a more comprehensive representation of ASD phenotypes than single-modality methods. Explainability and privacy-preserving learning have also emerged as important considerations for clinical deployment. However, many reported high-performance models are based on small sample sizes, repeated use of the ABIDE dataset, class imbalance, single-site validation, or limited external testing, raising concerns regarding generalizability. Beyond diagnostic accuracy, this review evaluates model interpretability, calibration, scalability, validation rigor, and clinical applicability. Overall, the analysis highlights the need for standardized benchmarks, externally validated multimodal datasets, clinically relevant evaluation metrics, and decision-support systems that complement rather than replace expert clinical assessment in ASD diagnosis and management.
S. K, Lakshmi Annapurna Y· Journal of Visualized Experi...· 0 citations
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.
Anupama N, Chandrashekar M. Patil· International Journal of Dev...· 0 citations
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.
Shabeena Lylath, Laxmi B. Rananavare· IAES International Journal o...· 0 citations
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