# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
###### Abstract  
Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual's educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual's ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN-LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU-CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models.  
AI fairness; deep learning; convolutional neural networks (CNN); particle swarm optimization (PSO); gated recurrent units (GRU) +
Footnote †: journal: _Future Internet_  
the disorder. Eye-tracking movements can provide visual preference patterns to identify autism in individuals [4]. Medical and sociodemographic features can also be investigated for diagnosing patients with autism [5]. Neurological studies on white matter microstructural disintegrity have also contributed to understanding the disorder [6]. Along the same lines, several clinical studies and machine-learning techniques have been deployed to study autism in individuals [7; 8]. While previous studies have presented the analysis of individuals using several methods, the studies lack extensive analysis for toddlers and adults. This paper presents two deep-learning architectures for identifying potential signs of autism in toddlers and adults. The first architecture incorporates a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) integrated with Particle Swarm Optimization (PSO). The second architecture integrates CNNs with Gated Recurrent Units (GRU). This study also incorporates visualization based on data for both toddlers and adults along with other baseline models such as K-Nearest Neighbors (KNN), Logistic regression (LR), Random forests (RF), Decision Trees (DT), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), CNN, LSTM, GRU, and CNN-LSTM. The performance of all these models has been evaluated across multiple datasets, i.e., for toddlers and adults, using specific metrics like accuracy, precision, recall, and F-1 scores. To the best of the author's knowledge, this is the first study to introduce CNN-LSTM-PSO-based architecture. The bio-inspired algorithm has been incorporated into the analysis to present the best optimal solution. Due to bias impacting ethical decision-making, this study deploys multiple Artificial Intelligence (AI) techniques to ensure the accurate performance of the models. Extensive feature engineering and techniques like SMOTE (Synthetic Minority Oversampling Technique) for handling imbalanced data are also part of this study. The rest of the paper is organized as follows: Material and Methods incorporate the related works and methodology. This section also discusses the architecture of the proposed deep learning methods followed by AI fairness techniques. The experimental analysis highlights the datasets used and the metrics considered for evaluation. The results and observations are discussed in the following experiments. This section also includes a comparative analysis of the proposed work with others. Finally, this study is concluded.