Prediction of Autism Spectrum Disorder Using Supervised Machine Learning Algorithms

Abstract

Autism appears to be a neuro developmental disorder that is visible in the early years. It is a wide-spectrum disorder that indicates that the severity and symptoms can vary from person to person. The Centre for Disease Control found that one in 68 was diagnosed with autism spectrum disorder with increasing numbers in every year. Detection of autism in adults is a cumbersome procedure because in adults, many symptoms can blend with some other mental health, motor impairment disorders so misinterpretation of actual diseases can in turn lead to a terrible life without proper diagnosis and effective treatment mechanisms. Machine learning is a powerful computer tool that supports different application domains Learning complex relationships or patterns from large datasets to draw accurate conclusions. Disease assessment can be done with predictive health data analysis and more appropriate treatment mechanisms that are now a hot area of research. Supervised learning is an important step of Machine learning which uses a rule-based approach by examining empirical data sets to build accurate predictive models. In this paper, decision tree, random forest, SVM, neural networks algorithms are applied on autism spectrum data which have been collected from UCI repository. The results of decision tree, random forest, SVM, neural networks algorithms on autism dataset are presented in this paper in an efficient manner. Analysis performed over these accurate results which will be useful to make right decisions in predicting autism spectrum disorder (ASD) at early stages. Thus, early autism intervention using machine learning techniques opens up a new way for autistic individuals to develop the potential to lead a better life by improving their behavioural and emotional skills.

Adaptive autism behavior prediction using improved binary whale optimization technique

“…Lakshmi Praveena et al has experimented with the supervised learning algorithms like RF, J48, SVM, and NN on ASD dataset 21 and obtained 100% result on J48 and random forest. Gomathi experimented with three classifiers 22 NB, J48 and k‐NN and obtained the better accuracy in J48 classifier.…”

A Correlation-Based Feature Selection and Classification Approach for Autism Spectrum Disorder

“…ManyresearchershavetriedtoautomatetheprocessofclassificationoftheASDandNon-ASD subjectswiththehelpofmachinelearningclassifiers,consideringthebehavioralandothertraitsofthe PredictionofAutismSpectrumDisorderUsingSupervisedMachineLearningAlgorithms (Praveena & Lakshmi, 2019) can classify the ASD and non-ASD subjects using Random Forest or Neural Network-basedclassifiers,withanaccuracyof100%.Buttheyhaveused22featuresforclassification, moreover,theyhavenotconsideredandfeatureselectionapproach.Thenumberoffeaturesandthe classifieritselfincreasesthecomplexityofthemethod.Therefore,itdoesnotseemtobeanideal method.…”

Exploratory Data Analysis and Decision Tree Modeling for Autism Spectrum Disorder: Machine Learning Approach

“…[2] Diabat and Shanableh demonstrated an ensemble learning model for testing autism in children. [3] Yet, another analysis by Praveena et al predicted autism using machine learning techniques. [4] The present study uses ASD screening data of adults to design the machine learning framework for classifying autism data.…”