# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 3 Methodology
### Implementing Feature Selection Methods
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Type** & **Description** \\ \hline A8: Answer of Q8 & Binary(0,1) & Would you describe your child’s first words as: (Toddler) When s/he was in preschool, s/he used to enjoy playing pretending games with other children (Child). S/he finds it difficult to imagine what it would be like to be someone else (Adolescent). I like to collect information about categories of things (Adult). & Would you describe your child’s first words as: (Toddler) When s/he was in preschool, s/he used to enjoy playing pretending games with other children (Child). S/he finds it difficult to imagine what it would be like to be someone else (Adolescent). I like to collect information about categories of things (Adult). & Would you describe your child’s first words as: (Toddler) When s/he was in preschool, s/he used to enjoy playing pretending games with other children (Child). S/he finds it difficult to imagine what it would be like to be someone else (Adolescent). I like to collect information about categories of things (Adult). & Would you describe your child’s first words as: (Toddler) When s/he was in preschool, s/he used to enjoy playing pretending games with other children (Child). S/he finds it difficult to imagine what it would be like to be someone else (Adolescent). I like to collect information about categories of things (Adult). & Would you describe your child’s first words as: (Toddler) When s/he finds it easy to work out what someone is thinking or feeling just by looking at their face (Child). S/he finds social situations easy (Adolescent). I find it easy to work out what someone is thinking or feeling just by looking at their face (Adult). & Would you describe your child state at nothing with no apparent purpose? (Toddler). \\ A10: Answer of Q10 & Binary(0,1) & Does your child state at nothing with no apparent purpose? (Toddler). S/he finds it hard to make new friends (Child, Adolescent). I find it difficult to work out people’s intentions (Adult). & Would you describe your child, Adolescent, and adult datasets. A value of 4 or higher was classified as ASD for the toddler dataset. In this work, we selected 16 features for the child, adolescent, and adult datasets and 15 features for the toddler dataset, respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._\[P_{t}(E)\subseteq_{i=1}^{M}[Et\frac{Ei}{N}+(1-Et)\frac{1}{D}] \tag{1}\]
* **Repeated Incremental Pruning to Produce Error Reduction (RIPPER)** is the rule induction algorithm that was introduced by [PERSON] in 1995 [PERSON] (1995). It is generated as a set of if-then-else rules that evolves several iterations of the rule learning algorithm. This model is maintained by three-steps such as grow, prune, and optimize [PERSON] (1995) where the evaluation process is denoted as, \[Gain(R_{0},R_{1})=c[log(\frac{T1}{T1+F1})-log(\frac{T0}{T0+F0})]\] (2) where \(R_{0}\) = the initial rule, \(R_{1}\) = the rule after adding conjunct, \(c\) = the number of true instances covered by \(R_{0}\) and \(R_{1}\), \(T_{0}\) = the number of true instances covered by \(R_{0}\), \(F_{0}\) = the number of false instances covered by \(R_{0}\), \(T_{1}\) = the number of true instances covered by \(R_{1}\), and \(F_{1}\) = the number of false instances covered by \(R_{1}\)[PERSON] (1995). * **Recursive Feature Elimination (RFE)** is a feature selection technique that discards the least important features recursively. In this process, the initial features are trained where each important feature is acquired through any selected attributes [PERSON] (1995). Then, the least important features are eliminated from the initial feature set. This process is recursively repeated until the desired number of feature subsets are obtained [PERSON] (1995). The steps of RFE are given as follows [PERSON] (1995): 1. Train the classifier. 2. Calculate the score for all features with the ranking. 3. Eliminate the feature with the lowest score.