A Multi-Model Ensemble Approach Using Deep and Traditional Learning for Autism Spectrum Disorder Classification

Abstract

Conventional diagnostic tests for Autism Spectrum Disorder (ASD) involve the use of subjective behavioral observations and questionnaires completed by the clinician, which can be time-consuming and subject to human bias. The challenge encourages the development of innovative, data-driven methods to facilitate early and accurate identification of ASD. The research proposes a Multi-Model Ensemble Approach Using Deep and traditional learning for ASD classification (MME-ASD) model. The MME-ASD model encompasses three traditional machine learning (ML) and two deep learning (DL) algorithms that perform according to a weighted majority voting strategy. The five learning paradigms are Random Forest (RF), Decision Tree (DT), Neural Network (NN), Convolutional Neural Network (CNN), and Deep Recurrent Neural Network (DRNN), which are utilized to enhance classification accuracy and generalization. An ensemble evaluation method is proposed to complete this study and assess the efficiency of the proposed MME-ASD model. The MME-ASD model acquires complementary properties by using numeric and textual data from a publicly available dataset of ASD, which includes information on 704 adults, both with and without a diagnosis. Initially, during the evaluation phase, the performance of the standalone traditional ML and DL algorithms was assessed across several train-test ratios. Subsequently, the proposed MME-ASD ensemble was evaluated with a 60-40 split to ensure compatibility with the baseline models. Finally, a 3-fold cross-validation experiment was conducted to assess the robustness and generalization of the proposed MME-ASD model. The experimental outcomes reveal that the MME-ASD model outperforms individual learners for both cross-validation and train-test assessments. It records evaluation metrics of accuracy 99.57%, precision 99.48%, and recall 98.94% across the 3-fold cross-validation experiments. The findings verify that incorporating deep and traditional learning models in an ensemble framework can significantly enhance the classification of ASD, offering a dependable and scalable computational model to aid clinical specialists in the initial diagnosis of ASD.