A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA

Abstract

Early Autism Spectrum Disorder (ASD) detection is crucial for promoting cognitive, motor skills, and social development. Artificial intelligence-powered systems present an exciting chance to transform ASD detection. The Autism Brain Imaging Data Exchange (ABIDE) represents a significant repository of brain imaging and phenotypic data collected from nineteen sites, encompassing a total of 1,114 cases of both ASD and typical control individuals. Each case includes 347 descriptive variables. This article demonstrates a case study on detecting ASD based on a machine learning (ML) pipeline utilizing phenotypic data from ABIDE. The ML pipeline involves four primary steps: (1) collecting and integrating data, (2) preprocessing the data, (3) training an ML model, and (4) evaluating the ML model. This article employs seven distinct ML algorithms for training the model and documenting the classification accuracy of each algorithm. During the case study, the accuracy ranged from 80.50% to 95.10%. The model trained using the random forest algorithm achieved the preeminent accuracy for ASD detection using phenotypic data.