# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 5 Discussion and Conclusions  
In this study, we proposed a machine learning model that analyzed ASD datasets of individual age groups (toddlers, children, adolescents, and adults) to detect autism at an early stage. This model used different feature selection methods like Boruta, CFS, RIPPER, and RFE to generate feature subsets. Then, NB, BG, CART, KNN, C4.5, KS, SVM, and RT were employed to classify autism at an early stage. When we evaluated the performance of them, SVM was the most stable classifier to explore the best result for different age groups, respectively. Along with SVM, KS, KNN, and NB showed better results to identify autism, respectively. On the other hand, individual classifiers presented their best performance in \(FS_{prr}\) for toddlers, \(FS_{bor}\) for adolescents, \(FS_{cfs}\) for children and adults, and \(FS_{bic}\) for children. Table 25 shows the comparison of the proposed model with related previous studies. Most of the existing works were worked with version-1 ASD datasets of [PERSON] et al. [6]. Besides, they did not properly focus on early detection of autism for toddlers and adolescents. Some works occurred with version-2 adolescent and adult datasets, whereas most of them did not conduct those works with more samples and machine learning approaches. [PERSON] et al. [14] used CHI and IG feature ranking methods into primary adolescent and \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **NB** & **BG** & **CART** & **KNN** & **C4.5** & **KS** & **SVM** & **RT** \\ \hline NB & & 0.0179 ** & 0.0179 ** & 0.0179 ** & 0.0149 ** & 0.0313 ** & 0.0313 ** & 0.0179 ** \\ BG & 1.000 & & 0.0179 ** & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 0.0179 ** \\ CART & 1.0000 & 1.0000 & & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 0.0179 ** \\ KNN & 1.0000 & 0.5183 & 0.0179 ** & & 1.0000 & 1.0000 & 1.0000 & 0.0179 ** \\ C4.5 & 1.0000 & 0.0100 ** & 0.0179 ** & 0.2236 & & 1.0000 & 0.2817 & 0.0179 ** \\ KS & 1.0000 & 0.0149 ** & 0.0100 ** & 0.0179 ** & 0.0179 ** & & 0.0313 ** & 0.0179 ** \\ SVM & 1.0000 & 0.1964 & 0.0313 ** & 0.6668 & 1.0000 & 1.0000 & & 0.0179 ** \\ RT & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & \\ \hline \hline \end{tabular} Note: Significant at 1% = ***,5% = **.**  
\end{table} 
Table 24: Pairwise WSR test for AUROC of adults. adult datasets to produce the most significant feature subsets. Then, they implemented logistic regression into primary datasets and subsets and observed the highest 99.91% accuracy for the adolescent dataset and a 97.58% accuracy for the adult dataset. In this work, we investigated all version-2 datasets of different age groups of [PERSON] et al. [14] where SVM achieved a 97.82% accuracy with \(FS_{rpr}\) for the toddler dataset, a 99.61% accuracy with \(FS_{cfs}\) and \(FS_{bic}\) for the child dataset, a 95.87% accuracy with \(FS_{bor}\) for the adolescent dataset, and a 96.82% accuracy for the adult dataset for \(FS_{cfs}\). Besides, we applied numerous feature selection and classification methods to justify these baselines and its feature sets more efficiently. In addition, a post hoc statistical significant test and SHAP interpretation method was used to evaluate deeply the outcomes of the proposed models. These types of evaluations did not properly occur in most of the existing works. In conclusions, the proposed machine learning framework was used to produce more-accurate and efficient results for early detection of ASD at individual age groups. Since diagnosing ASD traits is an expensive and time-consuming procedure, it is often postponed due to the difficulty of detecting autism in children and adolescents. In this process, machine learning models are efficient to detect autism at an early stage very efficiently. However, this model was not trained with various multivariate/dimensional datasets and explored significant attributes. In the future, we will integrate this framework with advanced technologies and develop a more-efficient ASD diagnosis system.