Diagnosis of Autism Spectrum Disorder Using Principal Component Analysis for Feature Extraction and Artificial Neural Networks

Autism spectrum disorder is one of the psychological disorders in children. Timely and accurate diagnosis of this disorder is of great importance in providing appropriate care and treatment for children. The main objective of this research is to emphasize the significance of features related to autism and their diagnosis using an intelligent model, as some of these features have higher priority.

For this purpose, the principal component analysis (PCA) method was employed to prioritize the features, and after extracting the optimal features, automatic disease diagnosis was performed using artificial neural networks.

The data used in this study were collected from the Kaggle dataset, including 1054 individuals, out of which 728 were diagnosed with autism and 326 were healthy. The results of this study indicate that the gradual elimination of features and reduction from 18 to 12 features can lead to achieving the same accuracy in diagnosing the autism spectrum using artificial neural networks.

Reducing the number of features in artificial intelligence models for autism diagnosis not only improves and optimizes the diagnostic process but also helps in reducing parental stress and preserving their privacy due to the reduced number of questions. Ultimately, this leads to the generation of models with better performance and interpretability.