Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques

Abstract

Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder (ASD) diseases. These diseases can affect the nerves at any stage of the human being in childhood, adolescence, and adulthood. ASD is known as a behavioral disease due to the appearances of symptoms over the first two years that continue until adulthood. Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD. The detection of ASD is a very challenging task among various researchers. Machine learning (ML) algorithms still act very intelligent by learning the complex data and predicting quality results. In this paper, ensemble ML techniques for the early detection of ASD are proposed. In this detection, the dataset is first processed using three ML algorithms such as sequential minimal optimization with support vector machine, Kohonen self-organizing neural network, and random forest algorithm. The prediction results of these ML algorithms (ensemble) further use the bagging concept called max voting to predict the final result. The accuracy, sensitivity, and specificity of the proposed system are calculated using confusion matrix. The proposed ensemble technique performs better than state-of-the art ML algorithms.