APPLICATION OF MACHINE LEARNING ALGORITHMS TO EVALUATE THE UCI DATABASE IN THE CLASSIFICATION OF AUTISM SPECTRUM DISORDERS
Keywords:Autism spectrum disorder, Machine learning algorithms, Screening autism spectrum disorder.
In this article, we present the results of an evaluation of the autism spectrum disorder classification (ASD) of children in the UCI database. We evaluated the data set with the SVM and Random Forest algorithms and also investigated the Decision Tree, Logistic Regression, K-Nearest-Neighbors, Naïve Bayes, and Multi-Layer Perceptron (MLP) algorithms. All algorithms give high classification results consistent with previous studies. We conclude that the data set for classifying children's autism spectrum disorders in the UCI database is reliable.
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