Performance Comparison of Deep Learning Algorithm for Autism Spectrum Disorder Prediction

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Autism Spectrum Disorder is a complex neurodevelopmental condition that significantly impacts social interaction and conduct in individuals. Early diagnosis and timely intervention are crucial for effectively managing the condition and enhancing overall outcomes. This study concentrates on creating predictive models through deep learning algorithms to assist in the early identification of ASD. The study employs an extensive dataset that covers various features linked to ASD traits, such as speech and language development, learning disorders, genetic factors, and behavioral characteristics. The investigation encompasses the assessment of three recurrent neural network (RNN) architectures-standard RNN, Long Short Term Memory(LSTM), and Gated Recurrent Unit(GRU) –for their effectiveness in predicting ASD. The dataset is split into 80% for training and 20% for testing. This study compares RNN, LSTM, and GRU algorithms. The classification results show that the LSTM and GRU models exhibited similar accuracy, with values of 71.03% and 70.78% respectively.