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Childhood Autism Spectrum Disorder Screening Using Machine Learning

EasyChair Preprint no. 7432

5 pagesDate: February 8, 2022

Abstract

A child/person having Autism Spectrum Disorder(ASD) which is a neurological/behavioral disorder will have an effect on their Interaction, Under Sensitivity for Sounds, Smells and touching senses, Over sensitivity, Social Skills, Repetitive and Restrictive Behaviors. Autism Spectrum Disorder(ASD) can be seen in children under or above age of 2 Years and continues to be in Adolescent and Adulthood age which is highly heritable. Cause for this disorder might be genetic susceptibility and environmental factors which is said to be a “Behavioral Disease”. Symptoms of this disease include Fever, Surgery, Age, Speech, etc. We have applied three algorithms Random Forest, KNN and Naïve Bayes Classifier to detect Autism Spectrum Disorder in Children. Random Forest Algorithm is our applied algorithm which boosts the model and gives the best result based on accuracy and Root Mean Square Error.

Keywords—Autism Spectrum Disorder, Machine Learning, Classification, KNN, Naïve Bayes , Random Forest.

Keyphrases: Autism Spectrum Disorder, Classification, KNN, machine learning, Naïve Bayes, Random Forest

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:7432,
  author = {R Monika and Hamsini R Koushik and B M Divya and Monisha K Shree and M D Deepak},
  title = {Childhood Autism Spectrum Disorder Screening Using Machine Learning},
  howpublished = {EasyChair Preprint no. 7432},

  year = {EasyChair, 2022}}
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