Awareness with Machine: Hybrid Approach to Detecting ASD with a Clustering

Detection of Autism Spectrum Disorder (ASD) is a crucial area of research, representing a foundational aspect of psychological studies. The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years. Interdisciplinary efforts have further propelled research into detection methods. Consequently, this study aims to contribute to both the fields of psychology and computer science. Specifically, the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder. This study is structured into two distinct phases: data preprocessing and classification. In the data preprocessing phase, four datasets—Toddler, Children, Adolescent, and Adult—were converted into numerical form, adjusted as necessary, and subsequently clustered. Clustering was performed using six different methods: K-means, agglomerative, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), mean shift, spectral, and Birch. In the second phase, the clustered ASD data were classified. The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation. In total, ten distinct machine learning algorithms were employed. The findings indicate that all clustering methods demonstrated success with various classifiers. Notably, the K-means algorithm emerged as particularly effective, achieving consistent and significant results across all datasets. This study is expected to serve as a guide for improving ASD detection performance, even with minimal data availability.