Unveiling Autism’s Patterns: The Deep Dynamic Levenberg–Marquardt Approach

Abstract

ASD (autism spectrum disorder) is a neurodevelopmental disorder affecting people’s social interaction, learning, and communication skills worldwide. It is a behaviorally distinct syndrome that is combined with several unknown and known disorders. The symptoms include sleep disorders, seizures, gastrointestinal tract symptoms, anxiety, wandering, hyperactivity/attention-deficit disorder, and obesity. Hence, early detection of ASD is significant. However, clinically standardized screening tests are considered a prolonged diagnostic time, which is prone to errors and also leads to a rise in medical costs. Therefore, to decrease the time required for diagnosis and improve the precision of the model, AI (artificial intelligence) (machine learning (ML)) techniques are used to complement other traditional methods. Hence, this study has proposed a modified deep dynamic Levenberg–Marquardt (DDLM) optimized approach, which enhances the accuracy and classifier’s precision for implementing binary classification of children with ASD and children without ASD and tackles the issues in early detection. The process starts by preprocessing the data using label encoding and feature scaling techniques for eradicating irrelevant and noisy data, and then classification proceeds by utilizing the modified DDLM model. The dataset used in the proposed model is an amalgamation of datasets, which are ASD meta-abundance and GSE113690_Autism_16S_rRNA. Additionally, a comparison of classifiers with three ML-based algorithms, namely, MLP (multilayer perceptron), NB (naïve Bayes), and XGBoost (extreme gradient boost), is performed to analyze the effectiveness of the proposed system in the binary classification of ASD. The efficacy of the proposed system is evaluated using performance factors such as specificity, precision, F1-score, accuracy, and recall.