Toddler ASD Classification Using Machine Learning Techniques

Ashima Sindhu Mohanty, Krishna Chandra Patra, Priyadarsan Parida


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.


ASD; Quantitative Checklist of Autism; Standardization; PCA; Machine Learning; Performance Parameters

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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