# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
## 5 Conclusions  
In this paper, machine-learning methods have been deployed to detect ASD in toddlers and adults. The analysis comprises performing data visualization to identify patterns in data, followed by an extensive study using ML models. Thirteen machine-learning models have been analyzed over two datasets, and the model performance has been evaluated using multiple statistical parameters. This study introduces two novel deep learning architectures, i.e., the CNN-LSTM-PSO model and the GRU-CNN model. It is observed that optimization techniques indeed lead to improvement in model accuracy. Moreover, several AI fairness techniques have been introduced to eliminate bias from the overall system. Some of the techniques include handling outliers, transformations, SMOTE, etc. Based on the experimental analysis and comparative analysis, it is observed that the proposed study advances the existing studies. Some limitations of the conducted research pertain to data availability, model complexity, and training time. Most of the datasets available for ASD research incorporate clinical data for adults and toddlers only. While we find enough data points to perform the analysis, it might not represent the actual number of people diagnosed with the disorder. Hence, the study can be improvised in the future by collecting more data. The models proposed for ASD identification in toddlers and adults are based on deep learning architectures incorporating multiple algorithms, layers, techniques, etc. Regularization techniques may likely overfit the model; hence, eliminating some layers may reduce the model's size and further improve its performance. Due to the involvement of hybrid architectures, multiple layers, and optimization techniques, the training time is comparatively high for deep learning networks. This may be evaded by deploying transfer learning models or other hybrid deep learning models in the future. Moreover, this study may introduce more hybrid deep neural networks and other optimization techniques like the Artificial Bee Colony, Grey Wolf Optimizer, Bat algorithm, etc. Studies show that autism is not confined to humans and may also be seen in animals like dogs and monkeys. Identifying the disorder using ML techniques may lead to a breakthrough in multiple fields.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The two datasets have been taken from Kaggle: Autism Screening Data for toddlers ([[https://www.kaggle.com/datasets/fabdelja/autism-screening-for-toddlers](https://www.kaggle.com/datasets/fabdelja/autism-screening-for-toddlers)]([https://www.kaggle.com/datasets/fabdelja/autism-screening-for-toddlers](https://www.kaggle.com/datasets/fabdelja/autism-screening-for-toddlers)), accessed on 1 August 2023) and Adults ([[https://www.kaggle.com/datasets/andrewmvd/autism-screening-on-adults](https://www.kaggle.com/datasets/andrewmvd/autism-screening-on-adults)]([https://www.kaggle.com/datasets/andrewmvd/autism-screening-on-adults](https://www.kaggle.com/datasets/andrewmvd/autism-screening-on-adults)) accessed on 1 August 2023).  
**Conflicts of Interest:** The author declares no conflict of interest.