Abstract
At present era, Autism Spectrum Disorder (ASD) has become one of the severe neurologically developed disorders throughout the world and early recognition can substantially get rid of this problem. The proposed work is based on the analysis of unbalanced ASD toddler dataset from UCI data repository. The work in this paper is performed in three stages. In first stage, the original data is preprocessed through converting the categorical attributes to numeric values by the process of frequency encoding followed by standardization of numeric attributes. In the second stage, the dimension of input is reduced using Principal component analysis (PCA). At the end, the classification of ASD Toddler data is performed through different machine learning classification models in two stages viz. through training parameter ε and through k-fold cross validation (k=10). The experimentation yields very high classification performance in comparison with other state-of-art approaches.
Highlights
Neuro-developmental disorder reflects mental illness where the nervous system is affected
This paper focuses on classification of Autism Spectrum Disorder (ASD) from no ASD traits using Machine Learning (ML)[6][7] among toddlers in the age group of 12-36 months
The confusion matrix describes the performance of the classification models up on a set of test data with known true values [5]
Summary
Neuro-developmental disorder reflects mental illness where the nervous system is affected. ASD is such a disorder where the social interaction, communication and behavior of an individual are in concern. It is characterized by repeated activities and aimless imaginary thoughts[1]. Upon all individuals including adult (17 years and above), adolescent (12 to 16 years), children (4 to 11 years) and toddlers (up to 36 months), ASD diagnosis can be implemented. It is not possible using conventional medical tests like blood test or body check-ups[4]. One of the reasons for not considering the toddler dataset in the studies is that major number of toddler cases have been found not associated with ASD which made the entire data set unbalanced
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