## 3 Related Work
### ASD Prediction Using DL Approach  
This study by [PERSON] et al. [27] proposed the IG/DBN model to diagnose ASD. They used DBN based on a Gaussian-Bernoulli Restricted Boltzmann Machine (GBRBM) as a classifier that employs deep learning for ASD classification. The IG filter was used as a gene selector to remove irrelevant genes, and to select the most relevant genes. They used a GE dataset that contains 30 samples and 43,931 features. The proposed model obtained a high accuracy of 98.64%. [PERSON] et al. [33] used the published ABIDE dataset, which includes a collection of structural (T1w) and functional (rsfMRI) brain images aggregated across 29 institutions. It includes 1028 participants diagnosed with autism. They explored various transformations that retain the maximum spatial resolution by summarizing the temporal dimension of the rsfMRI data, thus enabling the creation of a full three-dimensional convolutional neural network (3D-CNN) on the ABIDE dataset. They also used the SVM algorithm on the same data set and obtained the highest efficiency at 63%. [PERSON] et al. [34] tested six different neural network methods for incorporating phenotypic data such as gender and age, with rsfMRI to classify ASD. They tested the proposed models by using ABIDE. The best model was combining the baseline model directly with raw phenotypic data, and 70.1% accuracy was achieved for ASD classification. From Table 1, it has been noticed that most of the previous studies applied ML classifiers without using any FS algorithms to build ASD predictive models. Some of these models achieved good result. In addition, there are five studies that have used simple FS with ML algorithms on two data types (PBC and MRI images) [25; 26; 27; 28; 29], and the MRI image-based models failed to achieve a high performance compared to the PBC data type. On the other hand, there was limited research on optimizing FS methods using bio-inspired evolutionary algorithms to improve ASD prediction in the literature. Some of these algorithms achieved good results, as follows:Binary Firefly improved the accuracy to reach 97.9% in [3] with 10 selected features out of 21. GBPSO enhanced the accuracy percentage to 92.1% in [30] with 200 selected features out of 9454. PSO also enhanced the accuracy to reach 91% on an MRI image dataset [32]. From the aforementioned previous studies, we noticed the following:  
1. Two methods used for predicting ASD: ML and DL. 2. Multiple ASD datasets such PBC, GE, and IMR brain images are widely used for ASD diagnosis. 3. The 10-fold cross-validation was the most used for dataset partitioning. 4. Bio-inspired algorithms proved their ability to enhance ASD prediction in three types of datasets. 5. MRI brain datasets, compared with the two other datasets types, did not show a high performance in ASD prediction when using ML or DL approaches. The investigation of optimized feature selection methods using bio-inspired algorithms is limited in the existing ASD research and it has not been well addressed so far in this field. GA [31], PSO [30], and Firefly [3] were the only three bio-inspired algorithms that examined ASD prediction. Over the past few years, there have been some new bio-inspired algorithms that have been developed and used to improve feature selection to solve the high dimensionality problem, especially for disease prediction such as cancer. There are a lot of studies that handled cancer prediction using gene expression profiles and bio-inspired algorithms with ML, such as the bat algorithm (BA), flower pollination algorithm (FPA), grey wolf optimization (GWO), and artificial bee colony (ABC). In [35], a new model was built to predict prostate cancer by using BA with KNN, and it reached a high accuracy 100% and the selected features (genes) were 6 from 500. In [14], GWO with a DT classifier was used to predict Leukemia cancer, they it 100% accuracy. In [36], ABC with NB classification was used to predict Leukemia cancer, which reached 98.68% accuracy with 12 selected features (genes).