A Machine Learning Way to Classify Autism Spectrum Disorder

Abstract

In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various methods, these investigations didn't give any complete decision about anticipating autism qualities regarding distinctive age groups. Accordingly, this paper plans to locate the best technique for ASD classi-fication out of SVM, K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), Stochastic gradient descent (SGD), Adaptive boosting (AdaBoost), and CN2 Rule Induction using 4 ASD datasets taken from UCI ML repository. The classification accuracy (CA) we acquired after experimentation is as follows: in the case of the adult dataset SGD gives 99.7%, in the adolescent dataset RF gives 97.2%, in the child dataset SGD gives 99.6%, in the toddler dataset Ada-Boost gives 99.8%. Autism spectrum quotients (AQs) varied among several sce-narios for toddlers, adults, adolescents, and children that include positive predic-tive value for the scaling purpose. AQ questions referred to topics about attention to detail, attention switching, communication, imagination, and social skills.

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

“…If all the possible features are fed into an ML algorithm, the processing time will be excessive, and the accuracy may only improve marginally. Therefore, one of the challenges in ML problems is to find the optimal number of features that can provide a reasonably good accuracy [24].…”

“…KNN is a data mining algorithm utilized for classification purposes. For example, it has been used as an ML algorithm to classify autism spectrum disorder among people belonging to different age groups [24]. It is suitable for this study because the target of the algorithm is to find out, that is, to classify, which project in the GCDAs is the Max Project for a student.…”

“…It is suitable for this study because the target of the algorithm is to find out, that is, to classify, which project in the GCDAs is the Max Project for a student. The KNN algorithm involves the following steps [24].…”

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

“…Data visualization refers to the techniques used to convey data or information by representing it as visual elements within graphics (Alieva, 2021;Sujatha et al, 2021). The objective is to provide a deeper understanding of a dataset by presenting its key aspects in a more intuitive and meaningful manner than raw numbers alone.…”

Facial-Based Autism Classification Using Support Vector Machine Method

“…In another study [14], the objective was to identify the best machine learning methods for classifying ASD using various algorithms, including Random Forest, SVM, Stochastic Gradient Descent (SGD), KNN, Naïve Bayes, Adaptive Boosting (AdaBoost), Objective, and CN2 Rule. The research utilized datasets from the UCI repository, covering data for adult children, adolescents, young children, and toddlers.…”