Unsupervised Clustering Approaches for Autism Screening: Achieving 95.31% Accuracy with a Gaussian Mixture Model

Abstract

Autism spectrum disorder (ASD) remains a challenging condition to diagnose effectively and promptly, despite global efforts in public health, clinical screening, and scientific research. Traditional diagnostic methods, primarily reliant on supervised learning approaches, presuppose the availability of labeled data, which can be both time-consuming and resource-intensive to obtain. Unsupervised learning, in contrast, offers a means of gaining insights from unlabeled datasets in a manner that can expedite or support the diagnostic process. This paper explores the use of four distinct unsupervised clustering algorithms K-Means, Gaussian Mixture Model (GMM), Agglomerative Clustering, and DBSCAN to analyze a publicly available dataset of 704 adult individuals screened for ASD. After extensive hyperparameter tuning via cross-validation, the study documents how the Gaussian Mixture Model achieved the highest clustering-to-label accuracy (95.31%) when mapped to the original ASD/NO classification (4). Other key performance metrics included the Adjusted Rand Index (ARI) and silhouette scores, which further illustrated the internal coherence of each cluster. The dataset underwent preprocessing procedures including data cleaning, label encoding of categorical features, and standard scaling, followed by a thorough cross-validation approach to assess and compare the four clustering methods (5). These results highlight the significant potential of unsupervised methods in assisting ASD screening, especially in contexts where labeled data may be sparse, uncertain, or prohibitively expensive to obtain. With continued methodological refinements, unsupervised approaches hold promise for augmenting early detection initiatives and guiding resource allocation to individuals at high risk.