Feature Selection for Autism Spectrum Disorder Prediction using LASSO Logistic Regression (Prakriti - The International Multidisciplinary Research Journal, 2026)
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition where early and reliable detection is essential for timely intervention. Machine learning methods are increasingly used to support psychiatric assessments, yet their effectiveness depends strongly on identifying the most relevant features. This study applies a single embedded feature selection approach—LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression—to the publicly available UCI Autism Screening Dataset for Children. The dataset, containing behavioral screening questions and demographic variables, was preprocessed and evaluated using stratified 10-fold cross-validation. LASSO was employed both as a classifier and as a feature selector, shrinking less informative coefficients to zero while retaining the most predictive attributes. Results show that LASSO successfully reduced the dimensionality of the dataset, maintaining strong predictive performance in terms of accuracy, recall, and F1-score. Importantly, family history of autism and specific behavioral responses emerged as consistently influential features. This focused study highlights the value of LASSO as a dual-purpose tool for prediction and feature selection in ASD research. The findings demonstrate that concise, interpretable feature sets can be derived without compromising accuracy, supporting the development of efficient and transparent diagnostic aids for clinical practice.
Keywords: Autism Spectrum Disorder (ASD); Machine Learning; Feature Selection; LASSO Logistic Regression; Embedded Methods; Psychiatric Screening; Predictive Modeling; Interpretability