Abstract

Autism Spectrum Disorder is a neurological disorder linked to brain development that impacts facial features. An extensive and intricate neurodevelopmental disorder, ASD first appeared in early childhood. For healthcare professionals to treat and care for patients in a timely and appropriate manner, early recognition of ASD is essential. Many machine learning algorithms have been explored to investigate the viability of diagnosing autism. But finding accurate and timely ways to identify autism is still quite difficult. To improve autism identification accuracy while reducing time consumption, a new method termed Radial Adaptive Feature Projection based Generalized Emphasis Boost Classification (RAFP-GEBC) is presented. The primary goal of the RAFP-GEBC technique is to increase the accuracy of autism identification by means of effective processing. In order to identify autism spectrum disorder, this technique uses EEG signals from a dataset and includes pre-processing, feature selection, and classification. The Radial Basis Kernel Adaptive Stromberg Wavelet Filtering approach is used in the pre-processing stage. Input EEG signals are cleaned, transformed, and arranged into an appropriate manner using this technology. EEG signals are broken down into discrete frequency components, and noise is removed from each component in turn. Contingency Correlative Projection Pursuit Regression is then used in the feature identification process. The most pertinent and instructive characteristics are found through this procedure to ensure an appropriate classification of autism. The suggested RAFP-GEBC technique's feature selection cuts down on the amount of time needed for autism identification. The time needed to detect autism is decreased by the GEBC approach. In conclusion, the Generalized Learning Vector Quantized Emphasis Boost method is used to classify data with distorted features. By using an ensemble machine learning technique called “boosting,” classification results are strengthened and patients with and without autism can be distinguished with the least amount of error. As a result, the RAFP-GEBC method delivers precise and error-free autism identification. Numerous factors are experimentally evaluated by many people. According to qualitative study, the RAFP-GEBC strategy outperforms other approaches in the detection of autism.

Authors

K.K. Vinoth Kumar, K.P. Lochanambal
Government Arts College, Udumalpet, India

Keywords

Autism Detection, Electroencephalogram (EEG) signals, Radial Basis Kernel Adaptive Strömberg Wavelet Filtering technique, Contingency Correlative Projection Pursuit Regression, Generalized Learning Vector Quantized Emphasis Boost Technique