Feature Selection and Dimension Reduction of Social Autism Data.

Feature Selection and Dimension Reduction of Social Autism Data. Pac Symp Biocomput. 2020;25:707-718 Authors: Washington P, Paskov KM, Kalantarian H, Stockham N, Voss C, Kline A, Patnaik R, Chrisman B, Varma M, Tariq Q, Dunlap K, Schwartz J, Haber N, Wall DP Abstract Autism Spectrum Disorder (ASD) is a complex neuropsychiatric condition with a highly heterogeneous phenotype. Following the work of Duda et al., which uses a reduced feature set from the Social Responsiveness Scale, Second Edition (SRS) to distinguish ASD from ADHD, we performed item-level question selection on answers to the SRS to determine whether ASD can be distinguished from non-ASD using a similarly small subset of questions. To explore feature redundancies between the SRS questions, we performed filter, wrapper, and embedded feature selection analyses. To explore the linearity of the SRS-related ASD phenotype, we then compressed the 65-question SRS into low-dimension representations using PCA, t-SNE, and a denoising autoencoder. We measured the performance of a multilayer perceptron (MLP) classifier with the top-ranking questions as input. Classification using only the top-rated question resulted in an AUC of over 92% for SRS-derived diagnoses and an AUC of over 83% for dataset-specific diagnoses. High redundancy of features have implications towards replacing the social behaviors that are targeted in behavioral diagnostics and interventions, where digital quantif...
Source: Pacific Symposium on Biocomputing - Category: Bioinformatics Tags: Pac Symp Biocomput Source Type: research