NON-PARAMETRIC RANDOMIZED TREE CLASSIFIER FOR DETECTION OF AUTISM DISORDER IN TODDLERS

Authors

  • Prajwala T R Assistant Professor, Department of Computer Science and Engineering PES University, India

DOI:

https://doi.org/10.29121/granthaalayah.v9.i10.2021.4341

Keywords:

Jaccard Score, ROC Curves, Spearman Correlation, Gini Coefficient, Extra Tree Classifier And Negative Mean Absolute Error

Abstract

Autism is a behavioral disorder seen in toddlers and adolescents. It is a disorder which concerns behavior of child, speech, social interaction of child as well as nonverbal communication of child is affected. The parents of affected children find it very cumbersome to manage the child. Detection of such anomalies is really important at early stages. This paper mainly focuses on early detection of autistic behavior in toddlers. There are various machine learning and deep learning algorithms. Non parametric Extreme randomized classifier is one such technique which helps in early detection of autistic behavior in toddlers. The various performance evaluation metrics used are Jaccard score, ROC Curves and Mean Squared Error. The Feature selection is done using spearman correlation to identify the features affecting the child most and represented in form of Heat map. Extra tree classifier proves a better algorithm in detection of autism at early stages of child development.

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Published

2021-11-03

How to Cite

T R, P. (2021). NON-PARAMETRIC RANDOMIZED TREE CLASSIFIER FOR DETECTION OF AUTISM DISORDER IN TODDLERS. International Journal of Research -GRANTHAALAYAH, 9(10), 205–210. https://doi.org/10.29121/granthaalayah.v9.i10.2021.4341