Missing Data Imputation Method for Autism Prediction

Abstract

Missing data imputation is essential task becauseremoving all records with missing values will discard useful information from other attributes. This paper estimates the performanceof prediction for autism dataset with imputed missing values. Statistical imputation methods like mean, imputation with zero or constant and machine learning imputation methods like K-nearest neighbour chained Equation methods were compared with the proposed deep learning imputation method. The predictions of patients with autistic spectrum disorder were measured using support vector machine for imputed dataset. Among the imputation methods, Deeplearningalgorithm outperformed statistical and machine learning imputation methods. The same is validated using significant difference in p values revealed using Friedman’s test.