Autism Spectrum Disorders (ASDs) are difficult to classify, however it needs with the purpose of physicians have correct training and knowledge. At the instant on the other hand ASD is classified much afterward than is essentially potential. Early on identification of ASD increases the overall mind health of the child. Consequently, in the direction of advantage autism patients through increasing their right to use toward treatments such as early involvement, plan toward begin a robust data mining methods used for autism classification by using detection and feature selection algorithms depending on information from ASD patients. The basic aim of this work is toward choose best optimal features toward conquer the learning problem and to go faster the knowledge ability of autistic children. In order to carryout optimal feature selection process, Firefly Algorithm (FA) is introduced which chooses the features from the ASD screening procedure. In this work, an accurate FA method is introduced for identifying the most important and choosing a best ASD feature subset. The feature selection algorithm is performed depending on the wrapper method, i.e. the FA and the Support Vector Machine (SVM) is developed for ASD classification correspondingly. The proposed SVM classifier might considerably shorten and abbreviate the step of ASD analysis. The results demonstrated that the proposed SVM with FA classifier has provided an improved classification results depending on the chosen features. The experimental results of that FA based feature selection performing better when compared to other algorithms.

Autism Spectrum Disorders (ASDs), Firefly Algorithm (FA), feature selection, Support Vector Machine (SVM), Medical forms, Classification

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