# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 4 Experimental Results  
In the primary stage, this study used the Classification and Regression Training (CARET) package in R for feature selection and classification tasks [55]. However, Boruta, CFS, RIPPER, and RFE methods were implemented to produce numerous feature subsets for different age groups (i.e., toddler, child, adolescent, and adult). Table 4 shows the details of these feature subsets, respectively. Then, different baselines (i.e., primary data) along with their feature subsets were scrutinized by eight classifiers such as NB, BG, CART, KNN, C4.5, KS, SVM, and RT, respectively. In this case, we considered the k-fold cross-validation technique for classification, where the value of k is regarded as 10. After the classification process, a non-parametric statistical WSR test was employed to evaluate the performance of individual classifiers using Knowledge Extraction based on Evolutionary Learning (KEEL) software [56]. Then, SHAP summary plots were generated using the shap package in Python for the best classifier and different subsets. Figure 2 shows the details of illustrations about how to interpret machine learning models and explore importance features for generating significant outputs.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{2}{c}{**FS Method**} & \multicolumn{2}{c}{**Selected Features**} \\ \hline Toddler & \(FS_{bor}\) & Boruta & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, jaundice, Family\_ASD \\  & \(FS_{cfs}\) & CFS & A1, A2, A4, A5, A6, A7, A8, A9, A10, Age, User \\  & \(FS_{rpr}\) & RIPPER & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Jaudice \\  & \(FS_{rfe}\) & RFE & A1, A2, A3, A4, A6, A7, A8, A9, A10, Gender, User \\  & \(FS_{bic}\) & \(FS_{bor}\) & Intersect \(FS_{rpr}\) & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age \\ \hline Child & \(FS_{bor}\) & Boruta & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Gender, Residence \\  & \(FS_{cfs}\) & CFS & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 \\  & \(FS_{rpr}\) & RIPPER & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, jaundice \\  & \(FS_{rfe}\) & RFE & A1, A4, A6, A7, A8, A9, A10, Ethnicity, Jaudice, Family\_ASD \\  & \(FS_{bic}\) & \(FS_{bor}\) & Intersect \(FS_{cfs}\) & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 \\ \hline Adolescent & \(FS_{bor}\) & Boruta & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Ethnicity, Residence, User \\  & \(FS_{cfs}\) & CFS & A1, A2, A3, A4, A5, A6, A7, A10, Ethnicity \\  & \(FS_{rpr}\) & RIPPER & A1, A2, A3, A4, A5, A6, A7, A8, A10, Age, Gender \\  & \(FS_{rfe}\) & RFE & A1, A2, A3, A4, A5, A6, A8, A10, Age, Gender, Ethnicity, Jaudice, Residence \\  & \(FS_{bic}\) & \(FS_{bor}\) & Intersect \(FS_{cfs}\) & A1, A2, A3, A4, A5, A6, A7, A8, A10, Age \\ \hline Adult & \(FS_{bor}\) & Boruta & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Ethnicity, Residence, User \\  & \(FS_{cfs}\) & CFS & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Residence, User \\  & \(FS_{rpr}\) & RIPPER & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Residence, User \\  & \(FS_{rfe}\) & RFE & A1, A2, A4, A6, A7, A8, A9, A10, Age, Residence, Used\_App\_Before \\  & \(FS_{bic}\) & \(FS_{bor}\) & Intersect \(FS_{rpr}\) & A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Residence, User \\ \hline \hline \end{tabular}
\end{table}
Table 4: The generated feature subsets of individual age groups.