# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 3 Methodology
### Apply Individual Classification Methods  
After generating various feature subsets, 30 widely used classifiers were implemented into all of these datasets and their subsets. Some classifiers that produced less than 70% accuracy were eliminated. Thus, the performance of NB, KS, C4.5, CART, SVM, KNN, Bagging (BG), and Random Tree (RT) were considered. Then, we compared their performance and detected the best classifier. In addition, it detects feature subsets for which the classifier shows the highest outcomes. Therefore, a brief description of them are given as follows:  
* **Naive Bayes (NB)** is a most constructive probabilistic classifier based on Bayes theorem [PERSON] (1973). It predicts target output more efficiently according to the foundation of the probability of an entity. The formula of Bayes theorem is given as, \[R(a|b)=\frac{R(b|a)R(a)}{R(b)}\] (3) The derivation form for NB is: \[R(a|b)=R(b_{1}|a)*R(b_{2}|a)*.....*R(b_{n}|a)*R(a)\] (4) Here, \(R(a\mid b)\) is the subsequent probability of the target class; \(R(a)\) is the earlier probability of the class; \(R(b\mid a)\) is the prospect of the predictor specified class; and \(R(b)\) is the earlier probability of the predictor (see Equation (3)).
* **K-Star (KS)** is an instance-based classifier that categorizes samples or instances by differentiating it based on pre-categorized samples [PERSON] (1995). Some similar functions are used to determine the class of test instances. It uses an entropy-based function, which differentiates from other instance-based learners [PERSON] (1995).
* **Decision Tree (C4.5)** is the extension version of the ID3 algorithm that uses the recursive divide and conquer method to produce the C4.5 decision tree [PERSON] (1995). When unknown data are found, this method predicts a target class by satisfying several conditions. C4.5 uses Information Gain (IG) that calculates the gain ratio by the following equation [PERSON] (1995),\[Gainratio(Attribute)=\frac{Gain(Attribute)}{SpiIInfo(Attribute)} \tag{5}\]
* **Classification and Regression Trees (CART)** use several classification and regression trees where classification trees are used to accumulate the finite number of unsorted values and determine the prediction errors. Besides, regression trees are employed for grouping sorted or ordered values and determining the prediction error by calculating the root squared difference between target and predicted values [43].
* **K-Nearest Neighbour (KNN)** is used for classifying instances based on their nearest neighbors [44]. It generally takes more than one neighbor and determines their distances using the Euclidean method, which is calculated with the following equation [45], \[D=\sqrt{(m_{1}-m_{2})^{2}+(n_{1}-n_{2})^{2}}\] (6) where, \(D\) is distance between \((m_{1},n_{1})and(m_{2},n_{2})\) points.
* **Support Vector Machine (SVM)** generates some vectors to create a decision boundary that separates n-dimensional space into classes. This decision boundary is called a hyperplane. In the general situation, two parallel hyperplanes are generated, which concurrently minimizes the classification error and maximizes the margin of classes. It is called a maximum margin classifier [46].
* **Bagging Classifier (BG)** is a parallel ensemble method that generates several random subsets from substitution of the original dataset. Then, we analyzed them using the base classifier and aggregated their predictions by voting [47]. It decreases the variance and correctly predicts the target outcome.
* **Random Tree (RT)** is a decision tree where a set of possible trees are randomly generated with \(K\) random features. The combination of large sets of random trees is generally produced with accurate predictions more efficiently [48].