Dimension Reduction of Phenotypic Data for Enhancing Autism Spectrum Disorder Detection

Abstract

Autism Spectrum Disorder (ASD) is a neurological pathology that affects the human brain and causes problems in human social development, communication, and motor skills. The Autism Brain Imaging Data Exchange (ABIDE) is a prominent collection of brain imaging data and phenotypic data from 19 different sites with a total of 1114 cases ranging between ASD and Typical Control individuals (TC). Each case has 347 variables that describe it. In this paper, these variables are analyzed with the aim of extracting and selecting the most significant features for ASD detection using a machine learning (ML) pipeline. ML pipeline consists of three main steps: (1) creating feature groups either by using grouping technique (feature selection) or by using principal component analysis technique (feature extraction), (2) building ML model using three different ML algorithms for each feature group, and (3) evaluating ML model and recording the classification accuracy obtained from each model. The group of features that scores the best classification accuracy will be selected. Our experiments demonstrate that selecting key features significantly improves classification accuracy and highlight the effect of missing data on the model accuracy even after filling them with the mean imputation technique. During the experiments, the accuracy is scaled from 67% to 94.1% by selecting the most notable group of features and running them on the perfect model.