Efficient machine learning models across multiple datasets for autism spectrum disorder diagnoses

Abstract

Autism Spectrum Disorder (ASD) presents a complex neurodevelopmental challenge with diverse behavioral symptoms that overlap with other mental disorders, complicating diagnosis. Challenges in existing systems include the complexity and diversity of ASD symptoms, as well as the need for innovative and efficient detection strategies across different age groups. The application of machine learning (ML) offers a promising avenue for revolutionizing ASD prediction and diagnosis. The proposed work aims to overcome these challenges by leveraging ML and Automated Machine Learning (AutoML) techniques, combined with comprehensive datasets and advanced analytics, to enhance ASD detection and prediction capabilities. This study introduces the Autism Spectrum Disorder Detection (ASDD) Framework, which systematically applies ML and AutoML across text-based datasets for various age groups. A combined dataset is generated through the concatenation of various data sources, enriching the diagnostic scope. The framework incorporates AutoML tools such as Lazy Predict, TPOT, and AutoKeras, alongside feature selection methods like Principal Component Analysis (PCA) and Chi-Square. It also employs robust hyperparameter optimization techniques, including Bayesian optimization, Random Search, and genetic algorithms, and uses advanced ensemble methods like voting and bagging to boost prediction accuracy. An empirical evaluation of 27 distinct ML models across seven datasets, including a synthesized dataset named DB8, demonstrates exceptional performance, with many models achieving 100% accuracy and perfect F1 scores in various contexts. These outcomes highlight the effectiveness of the proposed framework in advancing ASD detection and prediction methodologies.